diff --git a/.github/scripts/close_duplicate_issues.py b/.github/scripts/close_duplicate_issues.py
deleted file mode 100755
index ec522af4f88..00000000000
--- a/.github/scripts/close_duplicate_issues.py
+++ /dev/null
@@ -1,230 +0,0 @@
-#!/usr/bin/env python3
-"""
-Detect and close duplicate GitHub issues using title similarity.
-
-Modes:
-  --scan           Compare all open issues against each other (batch)
-  --issue-number N Check a single issue against older open issues
-
-Requires the `gh` CLI to be authenticated.
-"""
-
-import argparse
-import difflib
-import json
-import re
-import subprocess
-import sys
-
-
-def normalize_title(title: str) -> str:
-    """Strip common prefixes, lowercase, and collapse whitespace."""
-    title = re.sub(
-        r"^\[?(bug|feature request|enhancement|question|docs)[:\]]?\s*",
-        "",
-        title,
-        flags=re.IGNORECASE,
-    )
-    return " ".join(title.lower().split())
-
-
-def gh(*args: str) -> str:
-    """Run a gh CLI command and return stdout."""
-    result = subprocess.run(
-        ["gh", *args],
-        capture_output=True,
-        text=True,
-        check=True,
-    )
-    return result.stdout
-
-
-def fetch_open_issues(repo: str | None) -> list[dict]:
-    """Fetch all open issues (excluding PRs) via gh api --paginate."""
-    if repo:
-        endpoint = (
-            f"repos/{repo}/issues?state=open&per_page=100&sort=created&direction=asc"
-        )
-    else:
-        endpoint = "repos/{owner}/{repo}/issues?state=open&per_page=100&sort=created&direction=asc"
-    cmd = ["api", "--paginate", endpoint]
-
-    raw = gh(*cmd)
-    # gh --paginate concatenates JSON arrays, so we may get multiple arrays
-    issues = []
-    for line in raw.strip().splitlines():
-        line = line.strip()
-        if not line:
-            continue
-        parsed = json.loads(line)
-        if isinstance(parsed, list):
-            issues.extend(parsed)
-        else:
-            issues.append(parsed)
-
-    # Filter out pull requests (they also appear in the issues endpoint)
-    return [i for i in issues if "pull_request" not in i]
-
-
-def close_as_duplicate(
-    issue_number: int, duplicate_of: int, repo: str | None, dry_run: bool
-) -> None:
-    """Close an issue as duplicate of another, adding a comment and label."""
-    repo_args = ["--repo", repo] if repo else []
-
-    if dry_run:
-        print(
-            f"  [DRY RUN] Would close #{issue_number} as duplicate of #{duplicate_of}"
-        )
-        return
-
-    # Add comment
-    comment_body = (
-        f"Closing as duplicate of #{duplicate_of}.\n\n"
-        "If you believe this is not a duplicate, please reopen and add context "
-        "explaining how this differs."
-    )
-    gh("issue", "comment", str(issue_number), "--body", comment_body, *repo_args)
-
-    # Add label
-    gh("issue", "edit", str(issue_number), "--add-label", "duplicate", *repo_args)
-
-    # Close with not_planned reason
-    gh(
-        "api",
-        f"repos/{repo or '{owner}/{repo}'}/issues/{issue_number}",
-        "-X",
-        "PATCH",
-        "-f",
-        "state=closed",
-        "-f",
-        "state_reason=not_planned",
-    )
-
-    print(f"  Closed #{issue_number} as duplicate of #{duplicate_of}")
-
-
-def find_duplicate(
-    issue: dict, candidates: list[dict], threshold: float
-) -> dict | None:
-    """Return the first candidate whose normalized title is above threshold."""
-    norm = normalize_title(issue["title"])
-    for candidate in candidates:
-        if candidate["number"] == issue["number"]:
-            continue
-        cand_norm = normalize_title(candidate["title"])
-        ratio = difflib.SequenceMatcher(None, norm, cand_norm).ratio()
-        if ratio >= threshold:
-            return candidate
-    return None
-
-
-def scan_all(
-    issues: list[dict], threshold: float, repo: str | None, dry_run: bool
-) -> int:
-    """Compare every issue against all older issues. Returns count of duplicates found."""
-    # Sort oldest first
-    issues.sort(key=lambda i: i["number"])
-    closed_count = 0
-
-    for idx, issue in enumerate(issues):
-        older = issues[:idx]
-        if not older:
-            continue
-        dup = find_duplicate(issue, older, threshold)
-        if dup:
-            ratio = difflib.SequenceMatcher(
-                None,
-                normalize_title(issue["title"]),
-                normalize_title(dup["title"]),
-            ).ratio()
-            print(
-                f"#{issue['number']}: \"{issue['title']}\"\n"
-                f"  -> duplicate of #{dup['number']}: \"{dup['title']}\" "
-                f"({ratio:.0%} similar)"
-            )
-            close_as_duplicate(issue["number"], dup["number"], repo, dry_run)
-            closed_count += 1
-
-    return closed_count
-
-
-def check_single(
-    issue_number: int,
-    issues: list[dict],
-    threshold: float,
-    repo: str | None,
-    dry_run: bool,
-) -> bool:
-    """Check a single issue against all older open issues. Returns True if duplicate found."""
-    target = None
-    for i in issues:
-        if i["number"] == issue_number:
-            target = i
-            break
-
-    if target is None:
-        print(f"Issue #{issue_number} not found among open issues.")
-        return False
-
-    older = [i for i in issues if i["number"] < issue_number]
-    dup = find_duplicate(target, older, threshold)
-    if dup:
-        ratio = difflib.SequenceMatcher(
-            None,
-            normalize_title(target["title"]),
-            normalize_title(dup["title"]),
-        ).ratio()
-        print(
-            f"#{target['number']}: \"{target['title']}\"\n"
-            f"  -> duplicate of #{dup['number']}: \"{dup['title']}\" "
-            f"({ratio:.0%} similar)"
-        )
-        close_as_duplicate(issue_number, dup["number"], repo, dry_run)
-        return True
-
-    print(f"#{issue_number}: no duplicate found above threshold {threshold}")
-    return False
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Detect and close duplicate GitHub issues"
-    )
-    mode = parser.add_mutually_exclusive_group(required=True)
-    mode.add_argument("--scan", action="store_true", help="Scan all open issues")
-    mode.add_argument("--issue-number", type=int, help="Check a single issue number")
-    parser.add_argument(
-        "--threshold", type=float, default=0.85, help="Similarity threshold (0-1)"
-    )
-    parser.add_argument(
-        "--close",
-        action="store_true",
-        help="Actually close duplicates (default is dry-run)",
-    )
-    parser.add_argument(
-        "--repo", type=str, help="Repository (owner/repo). Auto-detected if omitted."
-    )
-    args = parser.parse_args()
-
-    dry_run = not args.close
-
-    if dry_run:
-        print("=== DRY RUN MODE (pass --close to actually close issues) ===\n")
-
-    print("Fetching open issues...")
-    issues = fetch_open_issues(args.repo)
-    print(f"Found {len(issues)} open issues.\n")
-
-    if args.scan:
-        count = scan_all(issues, args.threshold, args.repo, dry_run)
-        print(f"\nTotal duplicates {'found' if dry_run else 'closed'}: {count}")
-    else:
-        found = check_single(
-            args.issue_number, issues, args.threshold, args.repo, dry_run
-        )
-        sys.exit(0 if found else 0)  # Always exit 0; finding no dup is not an error
-
-
-if __name__ == "__main__":
-    main()
diff --git a/.github/workflows/auto-close-duplicates.yml b/.github/workflows/auto-close-duplicates.yml
new file mode 100644
index 00000000000..d8256917805
--- /dev/null
+++ b/.github/workflows/auto-close-duplicates.yml
@@ -0,0 +1,69 @@
+name: Auto-close duplicate issues
+
+on:
+  schedule:
+    - cron: "0 9 * * *"
+  workflow_dispatch:
+    inputs:
+      dry_run:
+        description: Log which issues would close without closing anything
+        type: boolean
+        default: true
+      grace_period_days:
+        description: Days a duplicate notice must go unanswered before the close
+        type: number
+        default: 3
+  pull_request:
+    paths:
+      - .github/workflows/auto-close-duplicates.yml
+      - scripts/auto-close-duplicates.ts
+      - scripts/auto-close-duplicates.test.ts
+
+permissions: {}
+
+jobs:
+  test:
+    if: github.event_name == 'pull_request'
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+    permissions:
+      contents: read
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0
+        with:
+          bun-version: "1.4.0"
+
+      - name: Test the sweep
+        run: bun test scripts/auto-close-duplicates.test.ts
+
+  sweep:
+    if: github.event_name != 'pull_request' && github.repository == 'BerriAI/litellm'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    permissions:
+      contents: read
+      issues: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0
+        with:
+          # Exact version, never latest: the next step holds an issues: write token
+          bun-version: "1.4.0"
+
+      - name: Close unanswered duplicates, reopen ones the reporter answered
+        run: bun run scripts/auto-close-duplicates.ts
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DRY_RUN: ${{ inputs.dry_run == true }}
+          GRACE_PERIOD_DAYS: ${{ inputs.grace_period_days }}
diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml
index 78198b2c7bb..41ec43a1d9b 100644
--- a/.github/workflows/check_duplicate_issues.yml
+++ b/.github/workflows/check_duplicate_issues.yml
@@ -1,12 +1,19 @@
 name: Check Duplicate Issues
 
+# Flagging only. "Auto-close duplicate issues" closes a flagged issue 3 days later,
+# and only when its title is identical to an older open issue and nobody replied.
+# The HTML marker below is the handshake between the two, so keep it in the template.
+
 on:
   issues:
     types: [opened, edited]
 
+permissions: {}
+
 jobs:
   check-duplicate:
     runs-on: ubuntu-latest
+    timeout-minutes: 5
     permissions:
       issues: write
       contents: read
@@ -19,35 +26,12 @@ jobs:
           threshold: 0.6
           reaction: eyes
           comment: |
-            **⚠️ Potential duplicate detected**
+            <!-- litellm:potential-duplicate candidates={{#issues}}{{number}},{{/issues}} -->
+            **Potential duplicate detected**
 
-            This issue appears similar to existing issue(s):
+            This looks similar to:
             {{#issues}}
-            - [#{{number}}]({{html_url}}) - {{title}} ({{accuracy}}% similar)
+            - #{{number}} - {{title}}
             {{/issues}}
 
-            Please review the linked issue(s) to see if they address your concern. If this is not a duplicate, please provide additional context to help us understand the difference.
-
-      - name: Checkout close script
-        if: github.event.action == 'opened'
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-        with:
-          sparse-checkout: .github/scripts
-          persist-credentials: false
-
-      - name: Set up Python
-        if: github.event.action == 'opened'
-        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
-        with:
-          python-version: "3.12"
-
-      - name: Auto-close if high-confidence duplicate
-        if: github.event.action == 'opened'
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          python3 .github/scripts/close_duplicate_issues.py \
-            --issue-number ${{ github.event.issue.number }} \
-            --repo ${{ github.repository }} \
-            --threshold 0.85 \
-            --close
+            If this is a duplicate, add a thumbs-up reaction to the existing issue and follow along there. When the title is identical to an older open issue, this issue closes automatically in 3 days unless someone responds. If it is not a duplicate, comment here or add a thumbs-down reaction to this comment and it stays open.
diff --git a/Dockerfile b/Dockerfile
index 675a79b0686..b3ee85e9ed1 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,10 +1,10 @@
 # syntax=docker/dockerfile:1.7
 
 # Base image for building
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 
 # Runtime image
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
 ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43
@@ -40,8 +40,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx
 RUN apk add --no-cache \
     bash \
     gcc \
-    python3 \
-    python3-dev \
+    python-3.13 \
+    python-3.13-dev \
     rust \
     openssl \
     openssl-dev \
@@ -49,10 +49,6 @@ RUN apk add --no-cache \
     npm \
     libsndfile
 
-# UV_PYTHON_DOWNLOADS=0 keeps the venv on the apk python3 above. Without it,
-# uv resolves requires-python to the newest allowed minor, downloads a managed
-# interpreter under /root/.local/share/uv that the runtime stage never
-# receives, and the copied venv's python symlink dangles at runtime.
 ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
     UV_LINK_MODE=copy \
     UV_PYTHON_DOWNLOADS=0 \
@@ -70,7 +66,7 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr
     --extra extra_proxy \
     --extra semantic-router \
     --extra saml \
-    --python python3
+    --python python3.13
 
 # Copy full source tree
 COPY . .
@@ -91,7 +87,7 @@ RUN uv sync --frozen --no-default-groups --no-editable \
     --extra extra_proxy \
     --extra semantic-router \
     --extra saml \
-    --python python3
+    --python python3.13
 
 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
     npm_config_cache=/root/.npm \
@@ -106,7 +102,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 USER root
 
 # node (without npm) is required by the prisma CLI at runtime
-RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile
+RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile
 
 WORKDIR /app
 ENV PATH="/app/.venv/bin:${PATH}" \
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 8aea8312df9..aa01b9fba8b 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -1,5 +1,5 @@
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 
 FROM $UV_IMAGE AS uvbin
@@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/
 # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi
 # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes.
 RUN for i in 1 2 3; do \
-      apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \
+      apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \
       [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \
       sleep 5; \
     done
@@ -46,7 +46,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         --extra proxy-runtime \
         --extra extra_proxy \
         --extra semantic-router \
-        --python python3
+        --python python3.13
 
 # Stage 2 — copy source and install the project + workspace members.
 COPY . .
@@ -57,7 +57,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         --extra proxy-runtime \
         --extra extra_proxy \
         --extra semantic-router \
-        --python python3
+        --python python3.13
 
 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
     npm_config_cache=/root/.npm \
@@ -71,7 +71,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 USER root
 
 RUN for i in 1 2 3; do \
-      apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \
+      apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \
       [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \
       sleep 5; \
     done
diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json
index d60c3e9c0af..229d1eca3e8 100644
--- a/basedpyright-code-budget.json
+++ b/basedpyright-code-budget.json
@@ -3,7 +3,7 @@
     "limit": 16171
   },
   "reportArgumentType": {
-    "limit": 2226
+    "limit": 2224
   },
   "reportAssignmentType": {
     "limit": 319
diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database
index 4243eea5796..c1348f68231 100644
--- a/docker/Dockerfile.database
+++ b/docker/Dockerfile.database
@@ -1,10 +1,10 @@
 # syntax=docker/dockerfile:1.7
 
 # Base image for building
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 
 # Runtime image
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
 ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43
@@ -39,18 +39,14 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx
 RUN apk add --no-cache \
     bash \
     gcc \
-    python3 \
-    python3-dev \
+    python-3.13 \
+    python-3.13-dev \
     openssl \
     openssl-dev \
     nodejs \
     npm \
     libsndfile
 
-# UV_PYTHON_DOWNLOADS=0 keeps the venv on the apk python3 above. Without it,
-# uv resolves requires-python to the newest allowed minor, downloads a managed
-# interpreter under /root/.local/share/uv that the runtime stage never
-# receives, and the copied venv's python symlink dangles at runtime.
 ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
     UV_LINK_MODE=copy \
     UV_PYTHON_DOWNLOADS=0 \
@@ -68,7 +64,7 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr
     --extra extra_proxy \
     --extra semantic-router \
     --extra saml \
-    --python python3
+    --python python3.13
 
 # Copy full source tree
 COPY . .
@@ -89,7 +85,7 @@ RUN uv sync --frozen --no-default-groups --no-editable \
     --extra extra_proxy \
     --extra semantic-router \
     --extra saml \
-    --python python3
+    --python python3.13
 
 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
     npm_config_cache=/root/.npm \
@@ -103,7 +99,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 USER root
 
 # node (without npm) is required by the prisma CLI at runtime
-RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile
+RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile
 
 WORKDIR /app
 ENV PATH="/app/.venv/bin:${PATH}" \
diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root
index 19ef97a4f46..2221435a83a 100644
--- a/docker/Dockerfile.non_root
+++ b/docker/Dockerfile.non_root
@@ -1,8 +1,8 @@
 # syntax=docker/dockerfile:1.7
 
 # Base images
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 ARG PROXY_EXTRAS_SOURCE=published
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
@@ -37,8 +37,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx
 
 RUN for i in 1 2 3; do \
       apk add --no-cache \
-        python3 \
-        python3-dev \
+        python-3.13 \
+        python-3.13-dev \
         gcc \
         rust \
         bash \
@@ -50,10 +50,6 @@ RUN for i in 1 2 3; do \
         npm && break || sleep 5; \
     done
 
-# UV_PYTHON_DOWNLOADS=0 keeps the venv on the apk python3 above. Without it,
-# uv resolves requires-python to the newest allowed minor, downloads a managed
-# interpreter under /root/.local/share/uv that the runtime stage never
-# receives, and the copied venv's python symlink dangles at runtime.
 ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
     UV_LINK_MODE=copy \
     UV_PYTHON_DOWNLOADS=0 \
@@ -74,7 +70,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
     --extra extra_proxy \
     --extra semantic-router \
     --extra saml \
-    --python python3
+    --python python3.13
 
 # Copy full source tree
 COPY . .
@@ -101,7 +97,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
         --extra extra_proxy \
         --extra semantic-router \
         --extra saml \
-        --python python3 \
+        --python python3.13 \
         --no-sources-package litellm-proxy-extras; \
     else \
       uv sync --frozen --no-default-groups --no-editable \
@@ -110,7 +106,7 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
         --extra extra_proxy \
         --extra semantic-router \
         --extra saml \
-        --python python3; \
+        --python python3.13; \
     fi
 
 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
@@ -129,7 +125,7 @@ RUN for i in 1 2 3; do \
       apk upgrade --no-cache && break || sleep 5; \
     done && \
     for i in 1 2 3; do \
-      apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \
+      apk add --no-cache python-3.13 bash openssl tzdata libsndfile nodejs && break || sleep 5; \
     done
 
 # Copy only what runtime needs. The application is installed inside the venv;
diff --git a/gateway/Dockerfile b/gateway/Dockerfile
index 235c535f9f1..308d70a6b26 100644
--- a/gateway/Dockerfile
+++ b/gateway/Dockerfile
@@ -1,5 +1,5 @@
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:57108e597a8cf3bd376b810f1c3539c21942daefa242cb9dddaae30f8aac735d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 
 FROM $UV_IMAGE AS uvbin
@@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/
 # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi
 # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes.
 RUN for i in 1 2 3; do \
-      apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \
+      apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \
       [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \
       sleep 5; \
     done
@@ -47,7 +47,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         --extra extra_proxy \
         --extra semantic-router \
         --extra bedrock-realtime \
-        --python python3
+        --python python3.13
 
 # Stage 2 — copy source and install the project + workspace members.
 COPY . .
@@ -59,7 +59,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         --extra extra_proxy \
         --extra semantic-router \
         --extra bedrock-realtime \
-        --python python3
+        --python python3.13
 
 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \
     npm_config_cache=/root/.npm \
@@ -73,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 USER root
 
 RUN for i in 1 2 3; do \
-      apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \
+      apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \
       [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \
       sleep 5; \
     done
diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py
index b09498f9292..3ac92b04c27 100644
--- a/litellm/integrations/otel/mappers/genai.py
+++ b/litellm/integrations/otel/mappers/genai.py
@@ -62,6 +62,8 @@ class GenAIMapper:
         GenAI.RESPONSE_TIME_TO_FIRST_CHUNK: lambda d: d.time_to_first_chunk_seconds,
         GenAI.USAGE_INPUT_TOKENS: lambda d: d.usage.input_tokens,
         GenAI.USAGE_OUTPUT_TOKENS: lambda d: d.usage.output_tokens,
+        GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS: lambda d: d.usage.cache_creation_input_tokens,
+        GenAI.USAGE_CACHE_READ_INPUT_TOKENS: lambda d: d.usage.cache_read_input_tokens,
         Error.TYPE: lambda d: d.error.error_type if d.error else None,
         Server.ADDRESS: lambda d: d.server.address if d.server else None,
         Server.PORT: lambda d: d.server.port if d.server else None,
diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py
index f70c777e1a7..d35405538f6 100644
--- a/litellm/integrations/otel/model/payloads.py
+++ b/litellm/integrations/otel/model/payloads.py
@@ -95,6 +95,22 @@ class LLMUsage:
     input_tokens: int | None = None
     output_tokens: int | None = None
     total_tokens: int | None = None
+    cache_creation_input_tokens: int | None = None
+    cache_read_input_tokens: int | None = None
+
+    @classmethod
+    def from_standard_logging_payload(cls, payload: StandardLoggingPayload) -> LLMUsage:
+        # Cache token counts only exist on the raw provider usage object under metadata
+        metadata: Final[Mapping[str, object]] = payload.get("metadata") or {}
+        raw_usage: Final = metadata.get("usage_object")
+        usage_object: Final[Mapping[str, object]] = raw_usage if isinstance(raw_usage, Mapping) else {}
+        return cls(
+            input_tokens=as_int(payload.get("prompt_tokens")),
+            output_tokens=as_int(payload.get("completion_tokens")),
+            total_tokens=as_int(payload.get("total_tokens")),
+            cache_creation_input_tokens=as_int(usage_object.get("cache_creation_input_tokens")),
+            cache_read_input_tokens=as_int(usage_object.get("cache_read_input_tokens")),
+        )
 
 
 @dataclass(frozen=True)
@@ -363,11 +379,7 @@ class LLMCallSpanData:
             response_model=context.response_model,
             response_id=as_str(response.get("id")),
             request_params=LLMRequestParams.from_model_parameters(params),
-            usage=LLMUsage(
-                input_tokens=as_int(payload.get("prompt_tokens")),
-                output_tokens=as_int(payload.get("completion_tokens")),
-                total_tokens=as_int(payload.get("total_tokens")),
-            ),
+            usage=LLMUsage.from_standard_logging_payload(payload),
             finish_reasons=finish_reasons,
             error=_parse_error(payload),
             response_cost=as_float(payload.get("response_cost")),
diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py
index 4ad0cb5d1b4..f7a6280f95b 100644
--- a/litellm/integrations/otel/model/semconv.py
+++ b/litellm/integrations/otel/model/semconv.py
@@ -110,6 +110,8 @@ class GenAI:
     # usage
     USAGE_INPUT_TOKENS: Final = "gen_ai.usage.input_tokens"
     USAGE_OUTPUT_TOKENS: Final = "gen_ai.usage.output_tokens"
+    USAGE_CACHE_CREATION_INPUT_TOKENS: Final = "gen_ai.usage.cache_creation.input_tokens"
+    USAGE_CACHE_READ_INPUT_TOKENS: Final = "gen_ai.usage.cache_read.input_tokens"
     # content (opt-in, gated by capture mode)
     INPUT_MESSAGES: Final = "gen_ai.input.messages"
     OUTPUT_MESSAGES: Final = "gen_ai.output.messages"
diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py
index 256bee7b348..c350bc5569e 100644
--- a/litellm/litellm_core_utils/token_counter.py
+++ b/litellm/litellm_core_utils/token_counter.py
@@ -693,6 +693,26 @@ def _count_document_tokens(
     )
 
 
+def _count_file_tokens(
+    file_value: object,
+    count_function: TokenCounterFunction,
+    use_default_image_token_count: bool,
+) -> int:
+    """An OpenAI `file` block is the chat-completions spelling of a document, so it prices like one."""
+    if not isinstance(file_value, Mapping):
+        return 0
+    filename: Final = file_value.get("filename")
+    file_data: Final = file_value.get("file_data")
+    name_tokens: Final = count_function(filename) if isinstance(filename, str) and filename else 0
+    if not isinstance(file_data, str) or not file_data:
+        return name_tokens
+    return name_tokens + calculate_img_tokens(
+        data=file_data,
+        mode="auto",
+        use_default_image_token_count=use_default_image_token_count,
+    )
+
+
 def _count_anthropic_content(
     content: Mapping[str, Any],
     count_function: TokenCounterFunction,
@@ -778,6 +798,12 @@ def _count_content_list(
                     use_default_image_token_count,
                     default_token_count,
                 )
+            elif c["type"] == "file":
+                num_tokens += _count_file_tokens(
+                    c.get("file"),
+                    count_function,
+                    use_default_image_token_count,
+                )
             elif c["type"] in ("tool_use", "tool_result"):
                 num_tokens += _count_anthropic_content(
                     c,
@@ -807,7 +833,7 @@ def _count_content_list(
                 raise ValueError(
                     f"Invalid content item type: {content_type}. "
                     f"Expected str or dict with 'type' field "
-                    f"(text, image_url, image, document, tool_use, tool_result, thinking, tool_reference)."
+                    f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)."
                 )
         return num_tokens
     except Exception as e:
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index db9c8a5cedd..395d99a4caa 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -1631,6 +1631,11 @@ class AmazonConverseConfig(BaseConfig):
                 bedrock_tool_config["toolChoice"] = tool_choice_values
                 self._drop_tool_choice_type_conflicting_with_tool_config(additional_request_params)
 
+        config_block_entries: Final = tuple(
+            (config_name, config_class, inference_params.pop(config_name, None))
+            for config_name, config_class in self.get_config_blocks().items()
+        )
+
         data: Final[CommonRequestObject] = {
             "inferenceConfig": self._transform_inference_params(inference_params=inference_params),
         }
@@ -1641,9 +1646,7 @@ class AmazonConverseConfig(BaseConfig):
         if system_content_blocks:
             data["system"] = system_content_blocks
 
-        # Handle all config blocks
-        for config_name, config_class in self.get_config_blocks().items():
-            config_value = inference_params.pop(config_name, None)
+        for config_name, config_class, config_value in config_block_entries:
             if config_value is not None:
                 data[config_name] = config_class(**config_value)
 
diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py
index 1b1ab80e85d..4d774f6f165 100644
--- a/litellm/llms/openai/common_utils.py
+++ b/litellm/llms/openai/common_utils.py
@@ -268,6 +268,7 @@ class BaseOpenAILLM:
             "max_retries",
             "organization",
             "api_base",
+            "workload_identity_config",
         )
         openai_client_fields: Final = (
             BaseOpenAILLM.get_openai_client_initialization_param_fields(client_type=client_type)
diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py
index 6e66c998acf..16fa0017b23 100644
--- a/litellm/llms/openai/openai.py
+++ b/litellm/llms/openai/openai.py
@@ -51,6 +51,7 @@ from .common_utils import (
     drop_params_from_unprocessable_entity_error,
     is_output_token_limit_error,
 )
+from .workload_identity import resolve_openai_workload_identity_config
 
 openaiOSeriesConfig: Final = OpenAIOSeriesConfig()
 openAIGPT5Config: Final = OpenAIGPT5Config()
@@ -349,6 +350,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
         client: OpenAI | AsyncOpenAI | None = None,
         shared_session: Optional["ClientSession"] = None,
     ) -> OpenAI | AsyncOpenAI | None:
+        workload_identity_config: Final = resolve_openai_workload_identity_config(api_key=api_key, api_base=api_base)
         client_initialization_params: Final[dict] = locals()
         if client is None:
             if not isinstance(max_retries, int):
@@ -364,28 +366,49 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
             if cached_client:
                 if isinstance(cached_client, OpenAI) or isinstance(cached_client, AsyncOpenAI):
                     return cached_client
-            http_client: Final[httpx.Client | httpx.AsyncClient | None] = (
-                OpenAIChatCompletion._get_async_http_client(shared_session=shared_session)
-                if is_async
-                else OpenAIChatCompletion._get_sync_http_client()
-            )
             if is_async:
-                _new_client: OpenAI | AsyncOpenAI = AsyncOpenAI(
-                    api_key=api_key,
-                    base_url=api_base,
-                    http_client=http_client,
-                    timeout=timeout,
-                    max_retries=max_retries,
-                    organization=organization,
+                async_http_client: Final = OpenAIChatCompletion._get_async_http_client(shared_session=shared_session)
+                http_client: httpx.Client | httpx.AsyncClient | None = async_http_client
+                _new_client: OpenAI | AsyncOpenAI = (
+                    AsyncOpenAI(
+                        workload_identity=workload_identity_config.to_sdk_workload_identity(),
+                        base_url=api_base,
+                        http_client=async_http_client,
+                        timeout=timeout,
+                        max_retries=max_retries,
+                        organization=organization,
+                    )
+                    if workload_identity_config is not None
+                    else AsyncOpenAI(
+                        api_key=api_key,
+                        base_url=api_base,
+                        http_client=async_http_client,
+                        timeout=timeout,
+                        max_retries=max_retries,
+                        organization=organization,
+                    )
                 )
             else:
-                _new_client = OpenAI(
-                    api_key=api_key,
-                    base_url=api_base,
-                    http_client=http_client,
-                    timeout=timeout,
-                    max_retries=max_retries,
-                    organization=organization,
+                sync_http_client: Final = OpenAIChatCompletion._get_sync_http_client()
+                http_client = sync_http_client
+                _new_client = (
+                    OpenAI(
+                        workload_identity=workload_identity_config.to_sdk_workload_identity(),
+                        base_url=api_base,
+                        http_client=sync_http_client,
+                        timeout=timeout,
+                        max_retries=max_retries,
+                        organization=organization,
+                    )
+                    if workload_identity_config is not None
+                    else OpenAI(
+                        api_key=api_key,
+                        base_url=api_base,
+                        http_client=sync_http_client,
+                        timeout=timeout,
+                        max_retries=max_retries,
+                        organization=organization,
+                    )
                 )
 
             ## SAVE CACHE KEY
diff --git a/litellm/llms/openai/responses/count_tokens/transformation.py b/litellm/llms/openai/responses/count_tokens/transformation.py
index 6b2f4535df1..88f04c59e01 100644
--- a/litellm/llms/openai/responses/count_tokens/transformation.py
+++ b/litellm/llms/openai/responses/count_tokens/transformation.py
@@ -4,7 +4,100 @@ OpenAI Responses API token counting transformation logic.
 This module handles the transformation of requests to OpenAI's /v1/responses/input_tokens endpoint.
 """
 
-from typing import Any, Final
+from collections.abc import Mapping, Sequence
+from typing import Any, Final, Literal
+
+from typing_extensions import ReadOnly, TypedDict
+
+
+class ResponsesInputTextPart(TypedDict):
+    type: ReadOnly[Literal["input_text"]]
+    text: ReadOnly[str]
+
+
+class ResponsesInputImagePart(TypedDict):
+    type: ReadOnly[Literal["input_image"]]
+    image_url: ReadOnly[str]
+    detail: ReadOnly[str]
+
+
+class ResponsesInputFilePart(TypedDict):
+    type: ReadOnly[Literal["input_file"]]
+    filename: ReadOnly[str]
+    file_data: ReadOnly[str]
+
+
+ResponsesInputPart = ResponsesInputTextPart | ResponsesInputImagePart | ResponsesInputFilePart
+
+ResponsesContentRole = Literal["user", "assistant"]
+
+
+def _chat_image_block_to_responses_part(image_url: object) -> ResponsesInputImagePart | None:
+    url: Final = image_url.get("url") if isinstance(image_url, Mapping) else image_url
+    if not isinstance(url, str) or not url:
+        return None
+    detail: Final = image_url.get("detail") if isinstance(image_url, Mapping) else None
+    part: Final[ResponsesInputImagePart] = {
+        "type": "input_image",
+        "image_url": url,
+        "detail": detail if isinstance(detail, str) and detail else "auto",
+    }
+    return part
+
+
+def _chat_file_block_to_responses_part(file_value: object) -> ResponsesInputFilePart | None:
+    """Only an inline file round trips: OpenAI rejects `file_data` without the `filename` beside it."""
+    if not isinstance(file_value, Mapping):
+        return None
+    filename: Final = file_value.get("filename")
+    file_data: Final = file_value.get("file_data")
+    if not isinstance(filename, str) or not filename or not isinstance(file_data, str) or not file_data:
+        return None
+    part: Final[ResponsesInputFilePart] = {
+        "type": "input_file",
+        "filename": filename,
+        "file_data": file_data,
+    }
+    return part
+
+
+def _chat_block_to_responses_part(block: object, role: ResponsesContentRole) -> ResponsesInputPart | None:
+    if isinstance(block, str):
+        bare: Final[ResponsesInputTextPart] = {"type": "input_text", "text": block}
+        return bare
+    if not isinstance(block, Mapping):
+        return None
+    match block.get("type"):
+        case "text":
+            text_value: Final = block.get("text")
+            text: Final[ResponsesInputTextPart] = {
+                "type": "input_text",
+                "text": text_value if isinstance(text_value, str) else "",
+            }
+            return text
+        case "image_url" if role == "user":
+            return _chat_image_block_to_responses_part(block.get("image_url"))
+        case "file" if role == "user":
+            return _chat_file_block_to_responses_part(block.get("file"))
+        case _:
+            return None
+
+
+def chat_content_blocks_to_responses_content(
+    content: Sequence[object],
+    role: ResponsesContentRole,
+) -> str | tuple[ResponsesInputPart, ...]:
+    """Text-only content collapses to a joined string, which every role accepts and counts identically.
+
+    Only a user turn may carry an image or file part: the Responses API rejects any part but
+    output_text and refusal inside an assistant turn.
+    """
+    parts: Final = tuple(
+        part for part in (_chat_block_to_responses_part(block, role) for block in content) if part is not None
+    )
+    if any(part["type"] != "input_text" for part in parts):
+        return parts
+    return "\n".join(part["text"] for part in parts if part["type"] == "input_text")
 
 
 class OpenAICountTokensConfig:
@@ -120,18 +213,13 @@ class OpenAICountTokensConfig:
                     instructions_parts.append("\n".join(text_parts))
             elif role == "user":
                 if isinstance(content, list):
-                    # Extract text from content blocks for Responses API
-                    text_parts = []
-                    for block in content:
-                        if isinstance(block, dict) and block.get("type") == "text":
-                            text_parts.append(block.get("text", ""))
-                        elif isinstance(block, str):
-                            text_parts.append(block)
-                    content = "\n".join(text_parts)
+                    content = chat_content_blocks_to_responses_content(content, "user")
                 input_items.append({"role": "user", "content": content})
             elif role == "assistant":
                 # Map tool_calls to Responses API function_call items
                 tool_calls = msg.get("tool_calls")
+                if isinstance(content, list):
+                    content = chat_content_blocks_to_responses_content(content, "assistant")
                 if content:
                     input_items.append({"role": "assistant", "content": content})
                 if tool_calls:
diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py
index eac844a790d..eadc087383a 100644
--- a/litellm/llms/openai/responses/transformation.py
+++ b/litellm/llms/openai/responses/transformation.py
@@ -21,6 +21,7 @@ from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import LlmProviders
 
 from ..common_utils import OpenAIError
+from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config
 
 OPENAI_RESPONSES_API_MIN_MAX_OUTPUT_TOKENS: Final = 16
 
@@ -392,6 +393,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         litellm_params = litellm_params or GenericLiteLLMParams()
         api_key = litellm_params.api_key or litellm.api_key or litellm.openai_key or get_secret_str("OPENAI_API_KEY")
         headers.setdefault("Content-Type", "application/json")
+        workload_identity_config: Final = (
+            resolve_openai_workload_identity_config(api_key=api_key, api_base=litellm_params.api_base)
+            if self.custom_llm_provider is LlmProviders.OPENAI
+            else None
+        )
+        if workload_identity_config is not None:
+            headers["Authorization"] = f"Bearer {get_workload_identity_bearer_token(workload_identity_config)}"
+            return headers
         headers["Authorization"] = f"Bearer {api_key}"
         return headers
 
diff --git a/litellm/llms/openai/workload_identity.py b/litellm/llms/openai/workload_identity.py
new file mode 100644
index 00000000000..ecec161ed46
--- /dev/null
+++ b/litellm/llms/openai/workload_identity.py
@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import TYPE_CHECKING, Final
+from urllib.parse import urlparse
+
+import litellm
+from litellm.secret_managers.main import get_secret_str, normalize_nonempty_secret_str
+
+from .common_utils import OpenAIError
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from openai.auth import SubjectTokenProvider, WorkloadIdentity, WorkloadIdentityAuth
+
+OPENAI_WIF_CLIENT_ID: Final = "litellm"
+_OPENAI_API_HOST: Final = "api.openai.com"
+_SDK_UPGRADE_MESSAGE: Final = (
+    "OpenAI workload identity federation requires openai>=2.32.0. "
+    "Upgrade the installed openai package to use OPENAI_IDENTITY_PROVIDER_ID / "
+    "OPENAI_SERVICE_ACCOUNT_ID / OPENAI_IDENTITY_TOKEN_FILE."
+)
+
+
+@dataclass(frozen=True, slots=True)
+class OpenAIWorkloadIdentityConfig:
+    identity_provider_id: str
+    service_account_id: str
+    token_file: str
+
+    def to_sdk_workload_identity(self) -> WorkloadIdentity:
+        k8s_token_provider: Final = _load_sdk_k8s_token_provider()
+        workload_identity: Final[WorkloadIdentity] = {
+            "client_id": OPENAI_WIF_CLIENT_ID,
+            "identity_provider_id": self.identity_provider_id,
+            "service_account_id": self.service_account_id,
+            "provider": k8s_token_provider(self.token_file),
+        }
+        return workload_identity
+
+
+def resolve_openai_workload_identity_config(
+    api_key: str | None,
+    api_base: str | None,
+) -> OpenAIWorkloadIdentityConfig | None:
+    static_api_key: Final = normalize_nonempty_secret_str(api_key) or normalize_nonempty_secret_str(
+        get_secret_str("OPENAI_API_KEY")
+    )
+    if static_api_key is not None:
+        return None
+    effective_api_base: Final = (
+        api_base or litellm.api_base or get_secret_str("OPENAI_BASE_URL") or get_secret_str("OPENAI_API_BASE")
+    )
+    if not _targets_openai_api(effective_api_base):
+        return None
+    identity_provider_id: Final = get_secret_str("OPENAI_IDENTITY_PROVIDER_ID")
+    service_account_id: Final = get_secret_str("OPENAI_SERVICE_ACCOUNT_ID")
+    token_file: Final = get_secret_str("OPENAI_IDENTITY_TOKEN_FILE")
+    if not identity_provider_id or not service_account_id or not token_file:
+        return None
+    return OpenAIWorkloadIdentityConfig(
+        identity_provider_id=identity_provider_id,
+        service_account_id=service_account_id,
+        token_file=token_file,
+    )
+
+
+def get_workload_identity_bearer_token(config: OpenAIWorkloadIdentityConfig) -> str:
+    return _workload_identity_auth(config).get_token()
+
+
+def _targets_openai_api(api_base: str | None) -> bool:
+    if api_base is None:
+        return True
+    parsed: Final = urlparse(api_base)
+    return parsed.scheme == "https" and parsed.hostname == _OPENAI_API_HOST
+
+
+@lru_cache(maxsize=16)
+def _workload_identity_auth(config: OpenAIWorkloadIdentityConfig) -> WorkloadIdentityAuth:
+    sdk_workload_identity_auth: Final = _load_sdk_workload_identity_auth()
+    return sdk_workload_identity_auth(workload_identity=config.to_sdk_workload_identity())
+
+
+def _load_sdk_workload_identity_auth() -> type[WorkloadIdentityAuth]:
+    try:
+        from openai.auth import WorkloadIdentityAuth as sdk_workload_identity_auth
+    except ImportError as e:
+        raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e
+    return sdk_workload_identity_auth
+
+
+def _load_sdk_k8s_token_provider() -> Callable[[str], SubjectTokenProvider]:
+    try:
+        from openai.auth import k8s_service_account_token_provider
+    except ImportError as e:
+        raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e
+    return k8s_service_account_token_provider
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index 75098515deb..aca257dc095 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -27,6 +27,15 @@ from .common_utils import (
     get_vertex_base_url,
 )
 
+
+def _graft_default_vertex_path(api_base: str, default_url: str) -> str:
+    parsed_api_base: Final = urlparse(api_base)
+    default_segments: Final = urlparse(default_url).path.lstrip("/").split("/")
+    graft_segments: Final = default_segments[1:] if default_segments[0] in ("v1", "v1beta1") else default_segments
+    grafted_path: Final = parsed_api_base.path.rstrip("/") + "/" + "/".join(graft_segments)
+    return parsed_api_base._replace(path=grafted_path).geturl()
+
+
 GOOGLE_IMPORT_ERROR_MESSAGE: Final = (
     "Google Cloud SDK not found. Install it with: pip install 'litellm[google]' or pip install google-cloud-aiplatform"
 )
@@ -621,8 +630,9 @@ class VertexBase:
 
         Handles custom api_base for:
         1. Gemini (Google AI Studio) - constructs /models/{model}:{endpoint}
-        2. Vertex AI with standard proxies - constructs {api_base}:{endpoint};
-           if api_base has no path (bare host), grafts the default vertex URL path onto it
+        2. Vertex AI with standard proxies - grafts the default vertex URL path onto the
+           api_base when its path is empty or only an API version (/v1, /v1beta1);
+           otherwise constructs {api_base}:{endpoint}
         3. Vertex AI with PSC endpoints - constructs full path structure
            {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint}
            (only when use_psc_endpoint_format=True)
@@ -669,10 +679,14 @@ class VertexBase:
                     )
                 elif urlparse(api_base).path in ("", "/"):
                     url = api_base.rstrip("/") + urlparse(url).path
+                elif urlparse(api_base).path.rstrip("/") in ("/v1", "/v1beta1") and "/projects/" in urlparse(url).path:
+                    url = _graft_default_vertex_path(api_base=api_base, default_url=url)
                 else:
                     url = f"{api_base}:{endpoint}"
             if stream is True:
-                url = url + "?alt=sse"
+                parsed_stream_url: Final = urlparse(url)
+                stream_query: Final = f"{parsed_stream_url.query}&alt=sse" if parsed_stream_url.query else "alt=sse"
+                url = parsed_stream_url._replace(query=stream_query).geturl()
         return auth_header, url
 
     def _get_token_and_url(
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 094ec6ca17a..c4fd7e5a936 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -3746,7 +3746,7 @@
         "output_cost_per_token": 0,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-services/",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/",
         "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/<deployment-name> where deployment-name is your Azure deployment (e.g., azure-model-router)"
     },
     "azure/eu/gpt-4o-2024-08-06": {
@@ -5348,7 +5348,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "azure",
         "mode": "audio_transcription",
-        "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/gpt-realtime-whisper",
+        "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/gpt-realtime-whisper",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -9127,7 +9127,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 1024,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
         "supports_embedding_image_input": true
     },
     "azure_ai/Cohere-embed-v3-multilingual": {
@@ -9138,7 +9138,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 1024,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
         "supports_embedding_image_input": true
     },
     "azure_ai/FLUX-1.1-pro": {
@@ -9154,7 +9154,7 @@
         "litellm_provider": "azure_ai",
         "mode": "image_generation",
         "output_cost_per_image": 0.04,
-        "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
         "supported_endpoints": [
             "/v1/images/generations"
         ]
@@ -9487,7 +9487,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_vision": true
@@ -9501,7 +9501,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 2.04e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_vision": true
@@ -9514,7 +9514,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 7.1e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -9563,7 +9563,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 1.6e-05,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -9574,7 +9574,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-8B-Instruct": {
@@ -9586,7 +9586,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 6.1e-07,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -9776,7 +9776,7 @@
         "supported_endpoints": [
             "/v1/ocr"
         ],
-        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/"
+        "source": "https://ai.azure.com/catalog/models/mistral-document-ai-2512"
     },
     "azure_ai/doc-intelligence/prebuilt-read": {
         "litellm_provider": "azure_ai",
@@ -9987,7 +9987,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 3072,
-        "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
         "supported_endpoints": [
             "/v1/embeddings"
         ],
@@ -10171,7 +10171,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 0.00971,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat"
+        "source": "https://ai.azure.com/catalog/models/jais-30b-chat"
     },
     "azure_ai/jamba-instruct": {
         "input_cost_per_token": 5e-07,
@@ -10228,7 +10228,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 4e-08,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10251,7 +10251,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10263,7 +10263,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10300,7 +10300,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice",
         "supports_function_calling": true
     },
     "azure_ai/mistral-small": {
@@ -12335,7 +12335,8 @@
         "output_cost_per_token": 1e-05,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "claude-haiku-4-5-20251001": {
         "deprecation_date": "2026-10-15",
@@ -17787,7 +17788,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "prompt_cache_min_tokens": 1024
+        "prompt_cache_min_tokens": 1024,
+        "deprecation_date": "2027-01-08"
     },
     "eu.anthropic.claude-opus-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -22863,7 +22865,7 @@
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -22917,7 +22919,7 @@
         "output_cost_per_reasoning_token": 9e-06,
         "output_cost_per_token": 9e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -22982,7 +22984,7 @@
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23041,7 +23043,7 @@
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23246,7 +23248,7 @@
         "mode": "chat",
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23330,7 +23332,7 @@
         "mode": "chat",
         "output_cost_per_reasoning_token": 9e-06,
         "output_cost_per_token": 9e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23393,7 +23395,7 @@
         "output_cost_per_token": 3.75e-06,
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23450,7 +23452,7 @@
         "output_cost_per_token": 3.75e-06,
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -25347,7 +25349,7 @@
         "supports_vision": true
     },
     "gpt-4o-audio-preview": {
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 2.5e-06,
         "litellm_provider": "openai",
@@ -25670,7 +25672,7 @@
         "supports_vision": true
     },
     "gpt-4o-mini-audio-preview": {
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "openai",
@@ -25708,7 +25710,7 @@
     "gpt-4o-mini-realtime-preview": {
         "cache_creation_input_audio_token_cost": 3e-07,
         "cache_read_input_token_cost": 3e-07,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "openai",
@@ -25807,7 +25809,8 @@
         "output_cost_per_token": 5e-06,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "gpt-4o-mini-tts": {
         "input_cost_per_token": 2.5e-06,
@@ -25829,7 +25832,7 @@
     },
     "gpt-4o-realtime-preview": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25848,7 +25851,7 @@
     },
     "gpt-4o-realtime-preview-2024-12-17": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25867,7 +25870,7 @@
     },
     "gpt-4o-realtime-preview-2025-06-03": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25946,7 +25949,8 @@
         "output_cost_per_token": 1e-05,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "gpt-image-1.5": {
         "cache_read_input_token_cost": 1.25e-06,
@@ -27097,7 +27101,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber",
+        "source": "https://developers.openai.com/api/docs/models/gpt-5.6-cyber",
         "supports_computer_use": true,
         "supports_parallel_function_calling": true
     },
@@ -27136,7 +27140,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/daybreak-red-latest",
+        "source": "https://developers.openai.com/api/docs/models/daybreak-red-latest",
         "supports_computer_use": true,
         "supports_parallel_function_calling": true
     },
@@ -27176,7 +27180,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/daybreak-blue-latest",
+        "source": "https://developers.openai.com/api/docs/models/daybreak-blue-latest",
         "supports_parallel_function_calling": true
     },
     "chat-latest": {
@@ -27188,7 +27192,7 @@
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 3e-05,
-        "source": "https://platform.openai.com/docs/models/chat-latest",
+        "source": "https://developers.openai.com/api/docs/models/chat-latest",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses"
@@ -30483,7 +30487,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30524,7 +30528,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30565,7 +30569,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30598,7 +30602,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text"
         ],
@@ -30614,7 +30618,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text"
         ],
@@ -30630,7 +30634,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text",
             "image"
@@ -30647,7 +30651,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text",
             "image"
@@ -32592,7 +32596,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-R1-0528": {
         "max_tokens": 164000,
@@ -32604,7 +32608,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": {
         "max_tokens": 128000,
@@ -32615,7 +32619,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-V3": {
         "max_tokens": 128000,
@@ -32626,7 +32630,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-V3-0324": {
         "max_tokens": 128000,
@@ -32637,7 +32641,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/google/gemma-3-27b-it": {
         "max_tokens": 128000,
@@ -32649,7 +32653,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Llama-3.3-70B-Instruct": {
         "max_tokens": 128000,
@@ -32660,7 +32664,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Llama-Guard-3-8B": {
         "max_tokens": 128000,
@@ -32670,7 +32674,7 @@
         "output_cost_per_token": 6e-08,
         "litellm_provider": "nebius",
         "mode": "chat",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-8B-Instruct": {
         "max_tokens": 128000,
@@ -32681,7 +32685,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-70B-Instruct": {
         "max_tokens": 128000,
@@ -32692,7 +32696,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-405B-Instruct": {
         "max_tokens": 128000,
@@ -32703,7 +32707,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/mistralai/Mistral-Nemo-Instruct-2407": {
         "max_tokens": 128000,
@@ -32714,7 +32718,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/NousResearch/Hermes-3-Llama-3.1-405B": {
         "max_tokens": 128000,
@@ -32725,7 +32729,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/nvidia/Llama-3.1-Nemotron-Ultra-253B-v1": {
         "max_tokens": 128000,
@@ -32736,7 +32740,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/nvidia/Llama-3.3-Nemotron-Super-49B-v1": {
         "max_tokens": 131072,
@@ -32747,7 +32751,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-235B-A22B": {
         "max_tokens": 262144,
@@ -32758,7 +32762,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-32B": {
         "max_tokens": 32768,
@@ -32769,7 +32773,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-30B-A3B": {
         "max_tokens": 32768,
@@ -32780,7 +32784,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-14B": {
         "max_tokens": 32768,
@@ -32791,7 +32795,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-4B": {
         "max_tokens": 32768,
@@ -32802,7 +32806,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/QwQ-32B": {
         "max_tokens": 32768,
@@ -32814,7 +32818,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-72B-Instruct": {
         "max_tokens": 128000,
@@ -32825,7 +32829,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-32B-Instruct": {
         "max_tokens": 128000,
@@ -32836,7 +32840,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-Coder-7B": {
         "max_tokens": 32768,
@@ -32847,7 +32851,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-VL-72B-Instruct": {
         "max_tokens": 131072,
@@ -32859,7 +32863,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2-VL-72B-Instruct": {
         "max_tokens": 131072,
@@ -32871,7 +32875,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2-VL-7B-Instruct": {
         "max_tokens": 131072,
@@ -32882,7 +32886,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/BAAI/bge-en-icl": {
         "max_tokens": 32768,
@@ -32891,7 +32895,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/BAAI/bge-multilingual-gemma2": {
         "max_tokens": 8192,
@@ -32900,7 +32904,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/intfloat/e5-mistral-7b-instruct": {
         "max_tokens": 32768,
@@ -32909,7 +32913,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nvidia.nemotron-nano-12b-v2": {
         "input_cost_per_token": 2e-07,
@@ -33650,7 +33654,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33663,7 +33667,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33676,7 +33680,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33733,7 +33737,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true,
@@ -33747,7 +33751,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": false,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33758,7 +33762,7 @@
         "max_input_tokens": 512,
         "mode": "embedding",
         "output_vector_size": 1024,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_vision": true
     },
     "oci/cohere.command-a-reasoning-08-2025": {
@@ -34655,7 +34659,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 2e-07,
-        "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b",
+        "source": "https://openrouter.ai/bytedance/ui-tars-1.5-7b",
         "supports_tool_choice": true
     },
     "openrouter/deepseek/deepseek-chat": {
@@ -34890,7 +34894,7 @@
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -34931,7 +34935,7 @@
         "output_cost_per_reasoning_token": 1.5e-06,
         "output_cost_per_token": 1.5e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -36016,7 +36020,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/deepseek-r1-distill-llama-70b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36030,7 +36034,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/llama-3-1-8b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36043,7 +36047,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-1-70b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": false,
         "supports_tool_choice": false
@@ -36056,7 +36060,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-3-70b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36069,7 +36073,7 @@
         "max_tokens": 127000,
         "mode": "chat",
         "output_cost_per_token": 1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-7b-instruct-v0-3",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36082,7 +36086,7 @@
         "max_tokens": 118000,
         "mode": "chat",
         "output_cost_per_token": 1.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-nemo-instruct-2407",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36095,7 +36099,7 @@
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.8e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-small-3-2-24b-instruct-2506",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -36109,7 +36113,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 6.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mixtral-8x7b-instruct-v0-1",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -36122,7 +36126,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 8.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-coder-32b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -36135,7 +36139,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 9.1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-vl-72b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false,
@@ -36149,7 +36153,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 2.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen3-32b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36163,7 +36167,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 4e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-120b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36177,7 +36181,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-20b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36191,7 +36195,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 2.9e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/llava-next-mistral-7b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false,
@@ -36205,7 +36209,7 @@
         "max_tokens": 256000,
         "mode": "chat",
         "output_cost_per_token": 1.9e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mamba-codestral-7b-v0-1",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -38341,7 +38345,7 @@
         "source": "https://docs.mistral.ai/capabilities/code_generation/"
     },
     "text-embedding-004": {
-        "deprecation_date": "2026-01-14",
+        "deprecation_date": "2027-04-01",
         "input_cost_per_character": 2.5e-08,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-embedding-models",
@@ -38966,7 +38970,7 @@
         "max_input_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 3.6e-06,
-        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
+        "source": "https://www.together.ai/models/qwen3-5-397b-a17b",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_prompt_caching": true,
@@ -43203,7 +43207,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 5e-07,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -43219,7 +43223,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 5e-07,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -43236,7 +43240,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -43252,7 +43256,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -43372,7 +43376,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.4,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43386,7 +43390,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.15,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43401,7 +43405,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.4,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43416,7 +43420,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.15,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -44120,7 +44124,8 @@
         "output_cost_per_second": 0.0001,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "xai/grok-3": {
         "cache_read_input_token_cost": 2e-07,
@@ -45108,7 +45113,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.1,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2",
         "supported_modalities": [
             "text"
         ],
@@ -45120,7 +45125,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.3,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2-pro",
         "supported_modalities": [
             "text"
         ],
@@ -45132,7 +45137,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.5,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2-pro",
         "supported_modalities": [
             "text"
         ],
@@ -49342,7 +49347,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper",
+        "source": "https://developers.openai.com/api/docs/models/gpt-realtime-whisper",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -50594,7 +50599,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50632,7 +50637,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50670,7 +50675,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50708,7 +50713,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -51457,7 +51462,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3.6-35b-a3b": {
         "max_tokens": 131072,
@@ -51470,7 +51475,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3-30b-a3b": {
         "max_tokens": 131072,
@@ -51483,7 +51488,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3-coder-30b-a3b": {
         "max_tokens": 131072,
@@ -51496,7 +51501,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": false,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/deepseek-v4-flash": {
         "max_tokens": 163840,
@@ -51509,7 +51514,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/minimax-m2.7": {
         "max_tokens": 1000192,
@@ -51522,7 +51527,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": false,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "darkbloom/gemma-4-26b": {
         "input_cost_per_token": 3e-08,
@@ -51626,7 +51631,7 @@
         "input_cost_per_second": 7.5e-05,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-transcribe",
+        "source": "https://developers.openai.com/api/docs/models/gpt-transcribe",
         "supported_endpoints": [
             "/v1/audio/transcriptions",
             "/v1/realtime/transcription_sessions"
@@ -51644,7 +51649,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-live-transcribe",
+        "source": "https://developers.openai.com/api/docs/models/gpt-live-transcribe",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -51665,7 +51670,7 @@
         "max_output_tokens": 2000,
         "max_tokens": 2000,
         "mode": "realtime",
-        "source": "https://platform.openai.com/docs/models/gpt-realtime-translate",
+        "source": "https://developers.openai.com/api/docs/models/gpt-realtime-translate",
         "supported_modalities": [
             "audio"
         ],
@@ -51693,7 +51698,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
-        "source": "https://docs.claude.com/en/docs/about-claude/models/overview",
+        "source": "https://platform.claude.com/docs/en/about-claude/models/overview",
         "supports_adaptive_thinking": true,
         "thinking_always_on": true,
         "supports_mid_conversation_system": true,
@@ -51732,7 +51737,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
-        "source": "https://docs.claude.com/en/docs/about-claude/models/overview",
+        "source": "https://platform.claude.com/docs/en/about-claude/models/overview",
         "supports_adaptive_thinking": true,
         "thinking_always_on": true,
         "supports_assistant_prefill": false,
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 0f2d97b8b1c..7075fa5a31a 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -422,6 +422,9 @@ class LiteLLMRoutes(enum.Enum):
         "/responses/{response_id}/cancel",
         "/v1/responses/{response_id}/cancel",
         "/openai/v1/responses/{response_id}/cancel",
+        "/responses/input_tokens",
+        "/v1/responses/input_tokens",
+        "/openai/v1/responses/input_tokens",
         # vector stores
         "/vector_stores",
         "/v1/vector_stores",
@@ -3549,6 +3552,19 @@ class AllCallbacks(LiteLLMPydanticObjectBase):
     )
 
 
+class SpendLogsRouterMetadata(TypedDict):
+    """
+    Router provenance stamped on spend logs for deployments flagged with
+    model_info.internal_router_model, correlating the requested model group
+    with the provider deployment that served the call
+    """
+
+    requested_model: ReadOnly[str | None]
+    selected_model: ReadOnly[str | None]
+    selected_provider: ReadOnly[str | None]
+    router_correlation_id: ReadOnly[str | None]
+
+
 class SpendLogsMetadata(TypedDict):
     """
     Specific metadata k,v pairs logged to spendlogs for easier cost tracking
@@ -3591,6 +3607,7 @@ class SpendLogsMetadata(TypedDict):
     compression_savings: CompressionSavingsMetadata | None
     autorouter_savings: ReadOnly[float | None]  # stamped by the logging payload; None = not auto-routed
     litellm_gateway_injected_cache: ReadOnly[str | None]
+    router_metadata: ReadOnly[SpendLogsRouterMetadata | None]  # None = deployment not flagged internal_router_model
 
 
 class SpendLogsPayload(TypedDict):
diff --git a/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py b/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py
index a6ea2e09583..68914a1989e 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py
@@ -156,6 +156,31 @@ def _header_value(headers: Mapping[str, str], key: str, default: str) -> str:
     return headers.get(key, default)
 
 
+def _is_image_part(item: object) -> bool:
+    """Whether a structured-message content part carries an image rather than text."""
+
+    if not isinstance(item, Mapping):
+        return False
+
+    part: Final[Mapping[object, object]] = item
+    return part.get("type") == "image_url"
+
+
+def _scannable_text(content: object) -> str:
+    """Flatten a structured message's content into the single string the v1 detection endpoint takes.
+
+    Image parts are dropped: the endpoint accepts one string, so an image would only reach it as
+    its stringified source (a base64 blob or a URL), which is not text the scanner can evaluate.
+    """
+
+    if not isinstance(content, list):
+        return str(content or "")
+
+    parts: Final[Sequence[object]] = content
+    text_parts: Final = [item for item in parts if not _is_image_part(item)]  # mutable-ok: sent as a list repr
+    return str(text_parts or "")
+
+
 def is_saas(host: str) -> bool:
     """Checks whether the connection is to the SaaS platform"""
 
@@ -270,7 +295,7 @@ class HiddenlayerGuardrail(CustomGuardrail):
                     "messages": [
                         {
                             "role": last_msg.get("role", "user"),
-                            "content": str(last_msg.get("content", "")),
+                            "content": _scannable_text(last_msg.get("content")),
                         }
                     ]
                 },
diff --git a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py
index fa1f9f3d36d..0aaba4016cd 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py
@@ -20,6 +20,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"
         guardrail_name=guardrail.get("guardrail_name", ""),
         event_hook=litellm_params.mode,
         default_on=litellm_params.default_on,
+        file_sanitization_fail_open=getattr(litellm_params, "file_sanitization_fail_open", None),
     )
     litellm.logging_callback_manager.add_litellm_callback(_prompt_security_callback)
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
index 809d5e0fb31..84c4f118b00 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
@@ -4,10 +4,12 @@ import os
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Final, Literal, Optional
 
+import httpx
 from fastapi import HTTPException
 from typing_extensions import ReadOnly, TypedDict
 
 from litellm._logging import verbose_proxy_logger
+from litellm.exceptions import Timeout as LiteLLMTimeout
 from litellm.integrations.custom_guardrail import (
     CustomGuardrail,
     log_guardrail_information,
@@ -24,6 +26,9 @@ if TYPE_CHECKING:
     from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
 
 
+_SANITIZE_FILE_FAIL_OPEN_TIMEOUT_SECONDS: Final = 30.0
+
+
 class PromptSecurityGuardrailMissingSecrets(Exception):
     pass
 
@@ -63,6 +68,13 @@ class _SanitizeStatusResponse(TypedDict, total=False):
     metadata: ReadOnly[_SanitizeMetadata]
 
 
+class _SanitizeResult(TypedDict):
+    action: ReadOnly[str]
+    content: ReadOnly[str | None]
+    metadata: ReadOnly[_SanitizeMetadata]
+    violations: ReadOnly[Sequence[str]]
+
+
 class PromptSecurityGuardrail(CustomGuardrail):
     @classmethod
     def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]:
@@ -79,6 +91,8 @@ class PromptSecurityGuardrail(CustomGuardrail):
         user: str | None = None,
         system_prompt: str | None = None,
         check_tool_results: bool | None = None,
+        file_sanitization_timeout: float = _SANITIZE_FILE_FAIL_OPEN_TIMEOUT_SECONDS,
+        file_sanitization_fail_open: bool | None = None,
         **kwargs,
     ):
         kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks()))
@@ -108,6 +122,8 @@ class PromptSecurityGuardrail(CustomGuardrail):
         # Configuration for file sanitization
         self.max_poll_attempts = 30  # Maximum number of polling attempts
         self.poll_interval = 2  # Seconds between polling attempts
+        self.file_sanitization_timeout = file_sanitization_timeout
+        self.file_sanitization_fail_open = file_sanitization_fail_open is not False
 
         super().__init__(**kwargs)
 
@@ -397,6 +413,39 @@ class PromptSecurityGuardrail(CustomGuardrail):
         Sanitize file content using Prompt Security API.
         Returns: dict with keys 'action', 'content', 'metadata'
         """
+        try:
+            return await asyncio.wait_for(
+                self._sanitize_file_content(file_data, filename, user_api_key_alias),
+                timeout=self.file_sanitization_timeout,
+            )
+        except (asyncio.TimeoutError, httpx.TimeoutException, LiteLLMTimeout) as exc:
+            if not self.file_sanitization_fail_open:
+                verbose_proxy_logger.error(
+                    "Prompt Security Guardrail: file sanitization for %s timed out with %s; failing closed",
+                    filename,
+                    type(exc).__name__,
+                )
+                raise HTTPException(status_code=408, detail="File sanitization timeout") from exc
+
+            verbose_proxy_logger.error(
+                "Prompt Security Guardrail: file sanitization for %s timed out with %s; failing open",
+                filename,
+                type(exc).__name__,
+            )
+            fail_open_result: Final[_SanitizeResult] = {
+                "action": "allow",
+                "content": None,
+                "metadata": {},
+                "violations": (),
+            }
+            return fail_open_result
+
+    async def _sanitize_file_content(
+        self,
+        file_data: bytes,
+        filename: str,
+        user_api_key_alias: str | None,
+    ) -> _SanitizeResult:
         headers: Final = {"APP-ID": self.api_key}
         if user_api_key_alias:
             headers["X-LiteLLM-Key-Alias"] = user_api_key_alias
diff --git a/litellm/proxy/management_endpoints/customer_endpoints.py b/litellm/proxy/management_endpoints/customer_endpoints.py
index 9ef3d2defef..d2d87331d55 100644
--- a/litellm/proxy/management_endpoints/customer_endpoints.py
+++ b/litellm/proxy/management_endpoints/customer_endpoints.py
@@ -626,11 +626,7 @@ async def update_end_user(
         # get non default values for key
         non_default_values: Final = dict[str, object]()
         for k, v in data_json.items():
-            if v is not None and v not in (
-                [],
-                {},
-                0,
-            ):  # models default to [], spend defaults to 0, we should not reset these values
+            if v is not None and ((isinstance(v, bool) and k in data.fields_set()) or v not in ([], {}, 0)):
                 non_default_values[k] = v
 
         ## Get end user table data ##
diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py
index 9c4c9948c10..c08ca5b7783 100644
--- a/litellm/proxy/management_endpoints/internal_user_endpoints.py
+++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py
@@ -1027,6 +1027,14 @@ async def user_info_v2(
     This is the v2 replacement for /user/info, designed to avoid the "god endpoint" problem
     where the old endpoint loaded all keys and teams into memory.
 
+    Note on `spend`: this is the user's running budget counter, which the budget reset job
+    resets whenever `budget_reset_at` elapses (see `budget_duration`): to zero by default,
+    or to the overage above `max_budget` when `budget_rollover` is enabled. It is NOT
+    lifetime or per-period historical spend. For historical spend over a date range, use
+    `/user/daily/activity` or `/user/daily/activity/aggregated`, which read daily spend
+    records that only ever accumulate and are never reset. The two values are expected to
+    diverge once a budget reset has occurred within the queried period.
+
     Access control:
     - Proxy admins can query any user
     - Team admins can query users within their teams
@@ -2726,6 +2734,11 @@ async def get_user_daily_activity(
 
     Meant to optimize querying spend data for analytics for a user.
 
+    Reads daily spend records that only ever accumulate and are never affected by budget
+    resets. Their total can legitimately exceed the `spend` field returned by
+    `/v2/user/info`, which is a running budget counter that every budget reset sets back
+    to zero (or to the overage above `max_budget` when `budget_rollover` is enabled).
+
     Returns:
     (by date)
     - spend
@@ -2839,6 +2852,11 @@ async def get_user_daily_activity_aggregated(
     """
     Aggregated analytics for a user's daily activity without pagination.
     Returns the same response shape as the paginated endpoint with page metadata set to single-page.
+
+    Reads daily spend records that only ever accumulate and are never affected by budget
+    resets. Their total can legitimately exceed the `spend` field returned by
+    `/v2/user/info`, which is a running budget counter that every budget reset sets back
+    to zero (or to the overage above `max_budget` when `budget_rollover` is enabled).
     """
     from litellm.proxy.proxy_server import prisma_client
 
diff --git a/litellm/proxy/management_endpoints/team_callback_endpoints.py b/litellm/proxy/management_endpoints/team_callback_endpoints.py
index 08346983f32..c2f5dbb4032 100644
--- a/litellm/proxy/management_endpoints/team_callback_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_callback_endpoints.py
@@ -252,7 +252,7 @@ async def add_team_callbacks(
     Use this if if you want different teams to have different success/failure callbacks
 
     Parameters:
-    - callback_name (Literal["langfuse", "langsmith", "gcs"], required): The name of the callback to add
+    - callback_name (str, required): The name of the callback to add, e.g. "langfuse", "langsmith", "gcs", "newrelic". The value is validated against the callbacks that support team-scoped credentials
     - callback_type (Literal["success", "failure", "success_and_failure"], required): The type of callback to add. One of:
         - "success": Callback for successful LLM calls
         - "failure": Callback for failed LLM calls
@@ -268,6 +268,8 @@ async def add_team_callbacks(
         - langsmith_api_key: The API key for the Langsmith callback
         - langsmith_project: The project for the Langsmith callback
         - langsmith_base_url: The base URL for the Langsmith callback
+        - newrelic_api_key: The ingest license key for the team's New Relic account; routes both LLM/agent traces and cost metrics to that account. Requires the proxy to run with LITELLM_OTEL_V2=true, otherwise this callback is rejected with a 400
+        - newrelic_region: The New Relic region for the team's account ("us" or "eu"), riding the team's own key
 
     Example curl:
     ```
diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py
index 5e56e822484..aa7595ed13d 100644
--- a/litellm/proxy/response_api_endpoints/endpoints.py
+++ b/litellm/proxy/response_api_endpoints/endpoints.py
@@ -1,14 +1,18 @@
 import asyncio
 import json
 import time
-from collections.abc import AsyncIterator, Mapping
+from collections.abc import AsyncIterator, Awaitable, Mapping
+from enum import Enum
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Final, NamedTuple, cast, get_args
+from typing import TYPE_CHECKING, Any, Final, NamedTuple, Protocol, cast, get_args
 from uuid import uuid4
 
 import fastapi
 from fastapi import APIRouter, Depends, HTTPException, Request, Response
+from fastapi.responses import JSONResponse
+from openai.types.responses.response_create_params import ResponseInputParam
 from starlette.websockets import WebSocket, WebSocketDisconnect
+from typing_extensions import ReadOnly, TypedDict
 
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_guardrail import ModifyResponseException
@@ -26,8 +30,13 @@ from litellm.proxy.common_utils.http_parsing_utils import (
     _read_request_body,
     _safe_set_request_parsed_body,
 )
-from litellm.types.llms.openai import REASONING_EFFORT, ResponsesAPIResponse
+from litellm.types.llms.openai import (
+    REASONING_EFFORT,
+    ResponsesAPIOptionalRequestParams,
+    ResponsesAPIResponse,
+)
 from litellm.types.responses.main import DeleteResponseResult
+from litellm.types.utils import TokenCountResponse
 
 if TYPE_CHECKING:
     from litellm.router import Router
@@ -35,7 +44,7 @@ if TYPE_CHECKING:
 router: Final = APIRouter()
 
 _user_api_key_auth_dep: Final = Depends(user_api_key_auth)
-_RESPONSES_TAGS: Final = ["responses"]  # mutable-ok: fastapi's route signature requires List[str] tags
+_RESPONSES_TAGS: Final[list[str | Enum]] = ["responses"]  # mutable-ok: fastapi's route signature requires list tags
 
 _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType(
     {
@@ -1017,6 +1026,152 @@ async def compact_response(
         )
 
 
+class _ResponsesApiErrorDetail(TypedDict):
+    message: ReadOnly[str]
+    type: ReadOnly[str]
+    param: ReadOnly[str | None]
+    code: ReadOnly[str | None]
+
+
+class _ResponsesApiErrorBody(TypedDict):
+    error: ReadOnly[_ResponsesApiErrorDetail]
+
+
+class _ResponsesInputTokensResult(TypedDict):
+    object: ReadOnly[str]
+    input_tokens: ReadOnly[int]
+
+
+class _TokenCountPayload(TypedDict):
+    model: ReadOnly[str]
+    messages: ReadOnly[tuple[Mapping[str, object], ...]]
+    tools: ReadOnly[object]
+
+
+class _TokenCounter(Protocol):
+    def __call__(self, request: TokenCountRequest, call_endpoint: bool) -> Awaitable[TokenCountResponse]: ...
+
+
+def _proxy_token_counter() -> _TokenCounter:
+    from litellm.proxy.proxy_server import token_counter
+
+    return token_counter
+
+
+_token_counter_dep: Final = Depends(_proxy_token_counter)
+
+
+def _responses_invalid_request_response(message: str, param: str | None, code: str | None) -> JSONResponse:
+    body: Final[_ResponsesApiErrorBody] = {
+        "error": {
+            "message": message,
+            "type": "invalid_request_error",
+            "param": param,
+            "code": code,
+        }
+    }
+    return JSONResponse(status_code=400, content=body)
+
+
+def _missing_responses_param_response(param: str) -> JSONResponse:
+    return _responses_invalid_request_response(
+        message=f"Missing required parameter: '{param}'.",
+        param=param,
+        code="missing_required_parameter",
+    )
+
+
+def _responses_input_as_token_count_messages(
+    input_value: str | ResponseInputParam,
+    instructions: str | None,
+) -> tuple[Mapping[str, object], ...]:
+    from litellm.responses.litellm_completion_transformation.transformation import (
+        LiteLLMCompletionResponsesConfig,
+    )
+
+    request_params: Final[ResponsesAPIOptionalRequestParams] = {"instructions": instructions}
+    transformed: Final = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages(
+        input=input_value,
+        responses_api_request=request_params,
+    )
+    return tuple(
+        message if isinstance(message, dict) else message.model_dump(exclude_none=True) for message in transformed
+    )
+
+
+@router.post(
+    "/v1/responses/input_tokens",
+    dependencies=(_user_api_key_auth_dep,),
+    tags=_RESPONSES_TAGS,
+)
+@router.post(
+    "/responses/input_tokens",
+    dependencies=(_user_api_key_auth_dep,),
+    tags=_RESPONSES_TAGS,
+)
+@router.post(
+    "/openai/v1/responses/input_tokens",
+    dependencies=(_user_api_key_auth_dep,),
+    tags=_RESPONSES_TAGS,
+)
+async def responses_input_tokens(
+    request: Request,
+    token_counter: _TokenCounter = _token_counter_dep,
+):
+    """
+    Count the input tokens of a Responses API request without calling the model.
+
+    Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens
+
+    ```bash
+    curl -X POST http://localhost:4000/v1/responses/input_tokens \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer sk-1234" \
+    -d '{
+        "model": "gpt-4o",
+        "input": "Hello, how are you?"
+    }'
+    ```
+
+    Returns: `{"object": "response.input_tokens", "input_tokens": <count>}`
+    """
+    data: Final = await _read_request_body(request=request)
+    model_name: Final = data.get("model")
+    input_value: Final = data.get("input")
+    if not isinstance(model_name, str) or not model_name:
+        return _missing_responses_param_response("model")
+    if input_value is None:
+        return _missing_responses_param_response("input")
+    if isinstance(input_value, (str, list)) and not input_value:
+        return _responses_invalid_request_response(
+            message="""One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""",
+            param=None,
+            code="missing_required_parameter",
+        )
+
+    try:
+        payload: Final[_TokenCountPayload] = {
+            "model": model_name,
+            "messages": _responses_input_as_token_count_messages(
+                input_value=input_value,
+                instructions=data.get("instructions"),
+            ),
+            "tools": data.get("tools"),
+        }
+        token_request: Final = TokenCountRequest.model_validate(payload)
+    except Exception as e:
+        return _responses_invalid_request_response(
+            message=f"Invalid request for token counting: {e}", param=None, code=None
+        )
+
+    token_response: Final = await token_counter(request=token_request, call_endpoint=True)
+    result: Final[_ResponsesInputTokensResult] = {
+        "object": "response.input_tokens",
+        "input_tokens": token_response.total_tokens,
+    }
+    return result
+
+
 @router.post(
     "/v1/responses/{response_id}/cancel",
     dependencies=[Depends(user_api_key_auth)],
diff --git a/litellm/proxy/spend_tracking/budget_reservation.py b/litellm/proxy/spend_tracking/budget_reservation.py
index 8b2a5dd9312..2d113cfe355 100644
--- a/litellm/proxy/spend_tracking/budget_reservation.py
+++ b/litellm/proxy/spend_tracking/budget_reservation.py
@@ -172,7 +172,14 @@ async def reserve_budget_for_request(
 ) -> dict | None:
     if valid_token is None or not RouteChecks.is_llm_api_route(route=route):
         return None
-    if route in {"/models", "/v1/models", "/utils/token_counter"}:
+    if route in {
+        "/models",
+        "/v1/models",
+        "/utils/token_counter",
+        "/responses/input_tokens",
+        "/v1/responses/input_tokens",
+        "/openai/v1/responses/input_tokens",
+    }:
         return None
     if get_model_from_request(request_body, route, llm_router=llm_router) is None:
         return None
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index 43709e4e6ff..9f718b7d20d 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -30,7 +30,7 @@ from litellm.litellm_core_utils.litellm_logging import (
     request_model_access_groups_from_litellm_params,
 )
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps, strip_null_bytes
-from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
+from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload, SpendLogsRouterMetadata
 from litellm.proxy.spend_tracking.spend_log_error_logger import spend_log_error
 from litellm.proxy.utils import PrismaClient, hash_token
 from litellm.types.utils import (
@@ -93,6 +93,24 @@ def _redact_logged_api_key(value: str | None, *, already_redacted: bool = False)
     return hash_token(stripped)
 
 
+def _get_router_metadata_for_spend_log(
+    metadata: Mapping[str, object] | None,
+    requested_model: str | None,
+    selected_model: str | None,
+    selected_provider: str | None,
+    router_correlation_id: str | None,
+) -> SpendLogsRouterMetadata | None:
+    model_info: Final = metadata.get("model_info") if metadata is not None else None
+    if not isinstance(model_info, Mapping) or model_info.get("internal_router_model") is not True:
+        return None
+    return SpendLogsRouterMetadata(
+        requested_model=requested_model or None,
+        selected_model=selected_model or None,
+        selected_provider=selected_provider or None,
+        router_correlation_id=router_correlation_id,
+    )
+
+
 def _get_spend_logs_metadata(
     metadata: dict | None,
     applied_guardrails: list[str] | None = None,
@@ -109,6 +127,7 @@ def _get_spend_logs_metadata(
     cost_breakdown: CostBreakdown | None = None,
     litellm_call_id: str | None = None,
     autorouter_savings: float | None = None,
+    router_metadata: SpendLogsRouterMetadata | None = None,
 ) -> SpendLogsMetadata:
     if metadata is None:
         return SpendLogsMetadata(
@@ -148,13 +167,17 @@ def _get_spend_logs_metadata(
             autorouter_savings=autorouter_savings,
             litellm_gateway_injected_cache=None,
             litellm_call_id=litellm_call_id,
+            router_metadata=router_metadata,
         )
     verbose_proxy_logger.debug(
         "getting payload for SpendLogs, available keys in metadata: " + str(list(metadata.keys()))
     )
 
     # Filter the metadata dictionary to include only the specified keys
-    clean_metadata: Final = SpendLogsMetadata(**{key: metadata.get(key) for key in SpendLogsMetadata.__annotations__})
+    clean_metadata: Final = SpendLogsMetadata(
+        **{key: metadata.get(key) for key in SpendLogsMetadata.__annotations__ if key != "router_metadata"},
+        router_metadata=router_metadata,
+    )
     _raw_key: Final = clean_metadata.get("user_api_key")
     _trusted_hash: Final = metadata.get("user_api_key_hash")
     _already_redacted: Final = (
@@ -375,6 +398,20 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs
         hidden_params: Final = standard_logging_payload.get("hidden_params", {})
         litellm_overhead_time_ms = hidden_params.get("litellm_overhead_time_ms")
 
+    custom_llm_provider: Final = (
+        kwargs.get("custom_llm_provider")
+        or _sl_attribution_fallback(standard_logging_payload, "custom_llm_provider")
+        or None
+    )
+    raw_model: Final = cast(str, kwargs.get("model") or "")
+    model_name: Final = (
+        standard_logging_payload.get("model") if standard_logging_payload is not None else None
+    ) or reconstruct_model_name(raw_model, custom_llm_provider, metadata or {})
+    litellm_call_id: Final = cast(
+        str | None,
+        kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"),
+    )
+
     # clean up litellm metadata
     clean_metadata = _get_spend_logs_metadata(
         metadata,
@@ -433,9 +470,13 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs
         autorouter_savings=(
             standard_logging_payload.get("autorouter_savings", None) if standard_logging_payload is not None else None
         ),
-        litellm_call_id=cast(
-            str | None,
-            kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"),
+        litellm_call_id=litellm_call_id,
+        router_metadata=_get_router_metadata_for_spend_log(
+            metadata=metadata,
+            requested_model=_model_group,
+            selected_model=model_name,
+            selected_provider=custom_llm_provider,
+            router_correlation_id=litellm_call_id,
         ),
     )
 
@@ -480,15 +521,6 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs
 
     # Extract agent_id for A2A requests (set directly on model_call_details)
     agent_id: Final[str | None] = kwargs.get("agent_id") or metadata.get("agent_id")
-    custom_llm_provider: Final = (
-        kwargs.get("custom_llm_provider")
-        or _sl_attribution_fallback(standard_logging_payload, "custom_llm_provider")
-        or None
-    )
-    raw_model: Final = cast(str, kwargs.get("model") or "")
-    model_name: Final = (
-        standard_logging_payload.get("model") if standard_logging_payload is not None else None
-    ) or reconstruct_model_name(raw_model, custom_llm_provider, metadata or {})
 
     try:
         payload: Final[SpendLogsPayload] = SpendLogsPayload(
diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py
index f39df38d069..4a416f61e1b 100644
--- a/litellm/responses/litellm_completion_transformation/transformation.py
+++ b/litellm/responses/litellm_completion_transformation/transformation.py
@@ -1629,6 +1629,8 @@ class LiteLLMCompletionResponsesConfig:
             file_dict["file_id"] = file_id
         if item.get("file_data"):
             file_dict["file_data"] = item["file_data"]
+        if item.get("filename"):
+            file_dict["filename"] = item["filename"]
 
         new_item: Final[dict[str, object]] = {"type": "file", "file": file_dict}
         if "cache_control" in item:
diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py b/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py
index 6e64f0f47a5..94f8161f44e 100644
--- a/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py
+++ b/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py
@@ -12,6 +12,10 @@ class PromptSecurityGuardrailConfigModel(GuardrailConfigModel):
         default=None,
         description="The API base for the Prompt Security guardrail. If not provided, the `PROMPT_SECURITY_API_BASE` environment variable is used.",
     )
+    file_sanitization_fail_open: bool = Field(
+        default=True,
+        description="Whether file sanitization timeouts allow the original file through instead of blocking the request.",
+    )
 
     @staticmethod
     def ui_friendly_name() -> str:
diff --git a/litellm/types/router.py b/litellm/types/router.py
index 97bd93f3f47..ab6c807ba20 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -189,6 +189,11 @@ class ModelInfo(MirroredPricingParams):
     # router-wide default.
     enable_tag_filtering: bool | None = None
 
+    # when True, calls routed to this deployment persist a router_metadata block
+    # (requested model group, selected model + provider, router correlation id)
+    # in the spend log row's metadata. Set it on every deployment of the group.
+    internal_router_model: bool | None = None
+
     def __init__(self, id: str | int | None = None, **params) -> None:
         if id is None:
             id = str(uuid.uuid4())  # Generate a UUID if id is None or not provided
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 094ec6ca17a..c4fd7e5a936 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -3746,7 +3746,7 @@
         "output_cost_per_token": 0,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-services/",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/",
         "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/<deployment-name> where deployment-name is your Azure deployment (e.g., azure-model-router)"
     },
     "azure/eu/gpt-4o-2024-08-06": {
@@ -5348,7 +5348,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "azure",
         "mode": "audio_transcription",
-        "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/gpt-realtime-whisper",
+        "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/gpt-realtime-whisper",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -9127,7 +9127,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 1024,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
         "supports_embedding_image_input": true
     },
     "azure_ai/Cohere-embed-v3-multilingual": {
@@ -9138,7 +9138,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 1024,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice",
         "supports_embedding_image_input": true
     },
     "azure_ai/FLUX-1.1-pro": {
@@ -9154,7 +9154,7 @@
         "litellm_provider": "azure_ai",
         "mode": "image_generation",
         "output_cost_per_image": 0.04,
-        "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
         "supported_endpoints": [
             "/v1/images/generations"
         ]
@@ -9487,7 +9487,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_vision": true
@@ -9501,7 +9501,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 2.04e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_vision": true
@@ -9514,7 +9514,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 7.1e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -9563,7 +9563,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 1.6e-05,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -9574,7 +9574,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-8B-Instruct": {
@@ -9586,7 +9586,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 6.1e-07,
-        "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice",
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -9776,7 +9776,7 @@
         "supported_endpoints": [
             "/v1/ocr"
         ],
-        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/"
+        "source": "https://ai.azure.com/catalog/models/mistral-document-ai-2512"
     },
     "azure_ai/doc-intelligence/prebuilt-read": {
         "litellm_provider": "azure_ai",
@@ -9987,7 +9987,7 @@
         "mode": "embedding",
         "output_cost_per_token": 0.0,
         "output_vector_size": 3072,
-        "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice",
         "supported_endpoints": [
             "/v1/embeddings"
         ],
@@ -10171,7 +10171,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 0.00971,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat"
+        "source": "https://ai.azure.com/catalog/models/jais-30b-chat"
     },
     "azure_ai/jamba-instruct": {
         "input_cost_per_token": 5e-07,
@@ -10228,7 +10228,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 4e-08,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10251,7 +10251,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10263,7 +10263,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview",
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
@@ -10300,7 +10300,7 @@
         "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
-        "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice",
+        "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice",
         "supports_function_calling": true
     },
     "azure_ai/mistral-small": {
@@ -12335,7 +12335,8 @@
         "output_cost_per_token": 1e-05,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "claude-haiku-4-5-20251001": {
         "deprecation_date": "2026-10-15",
@@ -17787,7 +17788,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "prompt_cache_min_tokens": 1024
+        "prompt_cache_min_tokens": 1024,
+        "deprecation_date": "2027-01-08"
     },
     "eu.anthropic.claude-opus-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -22863,7 +22865,7 @@
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -22917,7 +22919,7 @@
         "output_cost_per_reasoning_token": 9e-06,
         "output_cost_per_token": 9e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -22982,7 +22984,7 @@
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23041,7 +23043,7 @@
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23246,7 +23248,7 @@
         "mode": "chat",
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23330,7 +23332,7 @@
         "mode": "chat",
         "output_cost_per_reasoning_token": 9e-06,
         "output_cost_per_token": 9e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23393,7 +23395,7 @@
         "output_cost_per_token": 3.75e-06,
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -23450,7 +23452,7 @@
         "output_cost_per_token": 3.75e-06,
         "output_cost_per_token_batches": 1.875e-06,
         "output_cost_per_token_flex": 1.875e-06,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -25347,7 +25349,7 @@
         "supports_vision": true
     },
     "gpt-4o-audio-preview": {
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 2.5e-06,
         "litellm_provider": "openai",
@@ -25670,7 +25672,7 @@
         "supports_vision": true
     },
     "gpt-4o-mini-audio-preview": {
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "openai",
@@ -25708,7 +25710,7 @@
     "gpt-4o-mini-realtime-preview": {
         "cache_creation_input_audio_token_cost": 3e-07,
         "cache_read_input_token_cost": 3e-07,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_token": 6e-07,
         "litellm_provider": "openai",
@@ -25807,7 +25809,8 @@
         "output_cost_per_token": 5e-06,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "gpt-4o-mini-tts": {
         "input_cost_per_token": 2.5e-06,
@@ -25829,7 +25832,7 @@
     },
     "gpt-4o-realtime-preview": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25848,7 +25851,7 @@
     },
     "gpt-4o-realtime-preview-2024-12-17": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25867,7 +25870,7 @@
     },
     "gpt-4o-realtime-preview-2025-06-03": {
         "cache_read_input_token_cost": 2.5e-06,
-        "deprecation_date": "2027-01-20",
+        "deprecation_date": "2026-05-07",
         "input_cost_per_audio_token": 4e-05,
         "input_cost_per_token": 5e-06,
         "litellm_provider": "openai",
@@ -25946,7 +25949,8 @@
         "output_cost_per_token": 1e-05,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "gpt-image-1.5": {
         "cache_read_input_token_cost": 1.25e-06,
@@ -27097,7 +27101,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber",
+        "source": "https://developers.openai.com/api/docs/models/gpt-5.6-cyber",
         "supports_computer_use": true,
         "supports_parallel_function_calling": true
     },
@@ -27136,7 +27140,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/daybreak-red-latest",
+        "source": "https://developers.openai.com/api/docs/models/daybreak-red-latest",
         "supports_computer_use": true,
         "supports_parallel_function_calling": true
     },
@@ -27176,7 +27180,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "supports_web_search": true,
-        "source": "https://platform.openai.com/docs/models/daybreak-blue-latest",
+        "source": "https://developers.openai.com/api/docs/models/daybreak-blue-latest",
         "supports_parallel_function_calling": true
     },
     "chat-latest": {
@@ -27188,7 +27192,7 @@
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 3e-05,
-        "source": "https://platform.openai.com/docs/models/chat-latest",
+        "source": "https://developers.openai.com/api/docs/models/chat-latest",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses"
@@ -30483,7 +30487,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30524,7 +30528,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30565,7 +30569,7 @@
             "search_context_size_low": 0.0025,
             "search_context_size_medium": 0.0025
         },
-        "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits",
+        "source": "https://ai.developer.meta.com/docs/pricing-rate-limits",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/responses",
@@ -30598,7 +30602,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text"
         ],
@@ -30614,7 +30618,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text"
         ],
@@ -30630,7 +30634,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text",
             "image"
@@ -30647,7 +30651,7 @@
         "max_output_tokens": 4028,
         "max_tokens": 4028,
         "mode": "chat",
-        "source": "https://llama.developer.meta.com/docs/models",
+        "source": "https://ai.developer.meta.com/docs/models",
         "supported_modalities": [
             "text",
             "image"
@@ -32592,7 +32596,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-R1-0528": {
         "max_tokens": 164000,
@@ -32604,7 +32608,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": {
         "max_tokens": 128000,
@@ -32615,7 +32619,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-V3": {
         "max_tokens": 128000,
@@ -32626,7 +32630,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/deepseek-ai/DeepSeek-V3-0324": {
         "max_tokens": 128000,
@@ -32637,7 +32641,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/google/gemma-3-27b-it": {
         "max_tokens": 128000,
@@ -32649,7 +32653,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Llama-3.3-70B-Instruct": {
         "max_tokens": 128000,
@@ -32660,7 +32664,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Llama-Guard-3-8B": {
         "max_tokens": 128000,
@@ -32670,7 +32674,7 @@
         "output_cost_per_token": 6e-08,
         "litellm_provider": "nebius",
         "mode": "chat",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-8B-Instruct": {
         "max_tokens": 128000,
@@ -32681,7 +32685,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-70B-Instruct": {
         "max_tokens": 128000,
@@ -32692,7 +32696,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/meta-llama/Meta-Llama-3.1-405B-Instruct": {
         "max_tokens": 128000,
@@ -32703,7 +32707,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/mistralai/Mistral-Nemo-Instruct-2407": {
         "max_tokens": 128000,
@@ -32714,7 +32718,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/NousResearch/Hermes-3-Llama-3.1-405B": {
         "max_tokens": 128000,
@@ -32725,7 +32729,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/nvidia/Llama-3.1-Nemotron-Ultra-253B-v1": {
         "max_tokens": 128000,
@@ -32736,7 +32740,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/nvidia/Llama-3.3-Nemotron-Super-49B-v1": {
         "max_tokens": 131072,
@@ -32747,7 +32751,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-235B-A22B": {
         "max_tokens": 262144,
@@ -32758,7 +32762,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-32B": {
         "max_tokens": 32768,
@@ -32769,7 +32773,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-30B-A3B": {
         "max_tokens": 32768,
@@ -32780,7 +32784,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-14B": {
         "max_tokens": 32768,
@@ -32791,7 +32795,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen3-4B": {
         "max_tokens": 32768,
@@ -32802,7 +32806,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/QwQ-32B": {
         "max_tokens": 32768,
@@ -32814,7 +32818,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-72B-Instruct": {
         "max_tokens": 128000,
@@ -32825,7 +32829,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-32B-Instruct": {
         "max_tokens": 128000,
@@ -32836,7 +32840,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-Coder-7B": {
         "max_tokens": 32768,
@@ -32847,7 +32851,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_function_calling": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2.5-VL-72B-Instruct": {
         "max_tokens": 131072,
@@ -32859,7 +32863,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2-VL-72B-Instruct": {
         "max_tokens": 131072,
@@ -32871,7 +32875,7 @@
         "mode": "chat",
         "supports_function_calling": true,
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/Qwen/Qwen2-VL-7B-Instruct": {
         "max_tokens": 131072,
@@ -32882,7 +32886,7 @@
         "litellm_provider": "nebius",
         "mode": "chat",
         "supports_vision": true,
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/BAAI/bge-en-icl": {
         "max_tokens": 32768,
@@ -32891,7 +32895,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/BAAI/bge-multilingual-gemma2": {
         "max_tokens": 8192,
@@ -32900,7 +32904,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nebius/intfloat/e5-mistral-7b-instruct": {
         "max_tokens": 32768,
@@ -32909,7 +32913,7 @@
         "output_cost_per_token": 0.0,
         "litellm_provider": "nebius",
         "mode": "embedding",
-        "source": "https://nebius.com/prices-ai-studio"
+        "source": "https://nebius.com/prices"
     },
     "nvidia.nemotron-nano-12b-v2": {
         "input_cost_per_token": 2e-07,
@@ -33650,7 +33654,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33663,7 +33667,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33676,7 +33680,7 @@
         "max_tokens": 4000,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33733,7 +33737,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": true,
         "supports_response_schema": false,
         "supports_native_streaming": true,
@@ -33747,7 +33751,7 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.56e-06,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_function_calling": false,
         "supports_response_schema": false,
         "supports_native_streaming": true
@@ -33758,7 +33762,7 @@
         "max_input_tokens": 512,
         "mode": "embedding",
         "output_vector_size": 1024,
-        "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/",
+        "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/",
         "supports_vision": true
     },
     "oci/cohere.command-a-reasoning-08-2025": {
@@ -34655,7 +34659,7 @@
         "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_token": 2e-07,
-        "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b",
+        "source": "https://openrouter.ai/bytedance/ui-tars-1.5-7b",
         "supports_tool_choice": true
     },
     "openrouter/deepseek/deepseek-chat": {
@@ -34890,7 +34894,7 @@
         "output_cost_per_reasoning_token": 3e-06,
         "output_cost_per_token": 3e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -34931,7 +34935,7 @@
         "output_cost_per_reasoning_token": 1.5e-06,
         "output_cost_per_token": 1.5e-06,
         "rpm": 2000,
-        "source": "https://ai.google.dev/pricing/gemini-3",
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
         "supported_endpoints": [
             "/v1/chat/completions",
             "/v1/completions",
@@ -36016,7 +36020,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/deepseek-r1-distill-llama-70b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36030,7 +36034,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/llama-3-1-8b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36043,7 +36047,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-1-70b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": false,
         "supports_tool_choice": false
@@ -36056,7 +36060,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 6.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-3-70b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36069,7 +36073,7 @@
         "max_tokens": 127000,
         "mode": "chat",
         "output_cost_per_token": 1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-7b-instruct-v0-3",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36082,7 +36086,7 @@
         "max_tokens": 118000,
         "mode": "chat",
         "output_cost_per_token": 1.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-nemo-instruct-2407",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true
@@ -36095,7 +36099,7 @@
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.8e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-small-3-2-24b-instruct-2506",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -36109,7 +36113,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 6.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mixtral-8x7b-instruct-v0-1",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -36122,7 +36126,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 8.7e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-coder-32b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -36135,7 +36139,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 9.1e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-vl-72b-instruct",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false,
@@ -36149,7 +36153,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 2.3e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/qwen3-32b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36163,7 +36167,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 4e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-120b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36177,7 +36181,7 @@
         "max_tokens": 131000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-20b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -36191,7 +36195,7 @@
         "max_tokens": 32000,
         "mode": "chat",
         "output_cost_per_token": 2.9e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/llava-next-mistral-7b",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false,
@@ -36205,7 +36209,7 @@
         "max_tokens": 256000,
         "mode": "chat",
         "output_cost_per_token": 1.9e-07,
-        "source": "https://endpoints.ai.cloud.ovh.net/models/mamba-codestral-7b-v0-1",
+        "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/",
         "supports_function_calling": false,
         "supports_response_schema": true,
         "supports_tool_choice": false
@@ -38341,7 +38345,7 @@
         "source": "https://docs.mistral.ai/capabilities/code_generation/"
     },
     "text-embedding-004": {
-        "deprecation_date": "2026-01-14",
+        "deprecation_date": "2027-04-01",
         "input_cost_per_character": 2.5e-08,
         "input_cost_per_token": 1e-07,
         "litellm_provider": "vertex_ai-embedding-models",
@@ -38966,7 +38970,7 @@
         "max_input_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 3.6e-06,
-        "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B",
+        "source": "https://www.together.ai/models/qwen3-5-397b-a17b",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
         "supports_prompt_caching": true,
@@ -43203,7 +43207,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 5e-07,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -43219,7 +43223,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 5e-07,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -43236,7 +43240,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -43252,7 +43256,7 @@
         "max_tokens": 2000000,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
-        "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)",
+        "source": "https://docs.x.ai/developers/models",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -43372,7 +43376,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.4,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43386,7 +43390,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.15,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43401,7 +43405,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.4,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -43416,7 +43420,7 @@
         "max_tokens": 1024,
         "mode": "video_generation",
         "output_cost_per_second": 0.15,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+        "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/veo/3-1-generate",
         "supported_modalities": [
             "text"
         ],
@@ -44120,7 +44124,8 @@
         "output_cost_per_second": 0.0001,
         "supported_endpoints": [
             "/v1/audio/transcriptions"
-        ]
+        ],
+        "deprecation_date": "2027-02-26"
     },
     "xai/grok-3": {
         "cache_read_input_token_cost": 2e-07,
@@ -45108,7 +45113,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.1,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2",
         "supported_modalities": [
             "text"
         ],
@@ -45120,7 +45125,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.3,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2-pro",
         "supported_modalities": [
             "text"
         ],
@@ -45132,7 +45137,7 @@
         "litellm_provider": "azure",
         "mode": "video_generation",
         "output_cost_per_video_per_second": 0.5,
-        "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation",
+        "source": "https://ai.azure.com/catalog/models/sora-2-pro",
         "supported_modalities": [
             "text"
         ],
@@ -49342,7 +49347,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper",
+        "source": "https://developers.openai.com/api/docs/models/gpt-realtime-whisper",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -50594,7 +50599,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50632,7 +50637,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50670,7 +50675,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -50708,7 +50713,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "source": "https://www.volcengine.com/docs/82379/1330310",
+        "source": "https://docs.volcengine.com/docs/82379/1330310",
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": false,
@@ -51457,7 +51462,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3.6-35b-a3b": {
         "max_tokens": 131072,
@@ -51470,7 +51475,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3-30b-a3b": {
         "max_tokens": 131072,
@@ -51483,7 +51488,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/qwen3-coder-30b-a3b": {
         "max_tokens": 131072,
@@ -51496,7 +51501,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": false,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/deepseek-v4-flash": {
         "max_tokens": 163840,
@@ -51509,7 +51514,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": true,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "pinstripes/ps/minimax-m2.7": {
         "max_tokens": 1000192,
@@ -51522,7 +51527,7 @@
         "supports_function_calling": true,
         "supports_assistant_prefill": true,
         "supports_reasoning": false,
-        "source": "https://pinstripes.io/pricing"
+        "source": "https://pinstripes.io/"
     },
     "darkbloom/gemma-4-26b": {
         "input_cost_per_token": 3e-08,
@@ -51626,7 +51631,7 @@
         "input_cost_per_second": 7.5e-05,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-transcribe",
+        "source": "https://developers.openai.com/api/docs/models/gpt-transcribe",
         "supported_endpoints": [
             "/v1/audio/transcriptions",
             "/v1/realtime/transcription_sessions"
@@ -51644,7 +51649,7 @@
         "input_cost_per_second": 0.0002833333333333333,
         "litellm_provider": "openai",
         "mode": "audio_transcription",
-        "source": "https://platform.openai.com/docs/models/gpt-live-transcribe",
+        "source": "https://developers.openai.com/api/docs/models/gpt-live-transcribe",
         "supported_endpoints": [
             "/v1/realtime",
             "/v1/realtime/transcription_sessions"
@@ -51665,7 +51670,7 @@
         "max_output_tokens": 2000,
         "max_tokens": 2000,
         "mode": "realtime",
-        "source": "https://platform.openai.com/docs/models/gpt-realtime-translate",
+        "source": "https://developers.openai.com/api/docs/models/gpt-realtime-translate",
         "supported_modalities": [
             "audio"
         ],
@@ -51693,7 +51698,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
-        "source": "https://docs.claude.com/en/docs/about-claude/models/overview",
+        "source": "https://platform.claude.com/docs/en/about-claude/models/overview",
         "supports_adaptive_thinking": true,
         "thinking_always_on": true,
         "supports_mid_conversation_system": true,
@@ -51732,7 +51737,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
-        "source": "https://docs.claude.com/en/docs/about-claude/models/overview",
+        "source": "https://platform.claude.com/docs/en/about-claude/models/overview",
         "supports_adaptive_thinking": true,
         "thinking_always_on": true,
         "supports_assistant_prefill": false,
diff --git a/scripts/auto-close-duplicates.test.ts b/scripts/auto-close-duplicates.test.ts
new file mode 100644
index 00000000000..b49bf05cbc2
--- /dev/null
+++ b/scripts/auto-close-duplicates.test.ts
@@ -0,0 +1,348 @@
+import { describe, expect, test } from "bun:test";
+
+import {
+  CLOSED_MARKER,
+  REOPEN_COMMENT,
+  candidateNumbers,
+  duplicateTarget,
+  normalizeTitle,
+  pendingNotice,
+  readConfig,
+  reopenTarget,
+  sweepClosedIssue,
+  sweepIssue,
+  type Comment,
+  type GitHubApi,
+  type Issue,
+  type Reaction,
+  type SweepConfig,
+} from "./auto-close-duplicates";
+
+const NOW = new Date("2026-09-04T09:00:00Z");
+const DAY_MS = 24 * 60 * 60 * 1000;
+const daysAgo = (days: number): string => new Date(NOW.getTime() - days * DAY_MS).toISOString();
+
+const issue = (number: number, title: string, overrides: Partial<Issue> = {}): Issue => ({
+  number,
+  title,
+  state: "open",
+  user: { login: "reporter" },
+  ...overrides,
+});
+
+const notice = (candidates: readonly number[], createdAt: string, overrides: Partial<Comment> = {}): Comment => ({
+  id: 900,
+  body: `<!-- litellm:potential-duplicate candidates=${candidates.join(",")}, -->\n**Potential duplicate detected**`,
+  created_at: createdAt,
+  user: { type: "Bot", login: "github-actions[bot]" },
+  ...overrides,
+});
+
+const humanComment = (createdAt: string, body = "It is not the same thing", login = "reporter"): Comment => ({
+  id: 901,
+  body,
+  created_at: createdAt,
+  user: { type: "User", login },
+});
+
+const config: SweepConfig = { repo: "BerriAI/litellm", graceDays: 3, dryRun: false, now: NOW };
+
+describe("normalizeTitle", () => {
+  test("drops the template prefix, case, and punctuation", () => {
+    expect(normalizeTitle("[Bug]: Gemma 4-e4b fails on Vertex!")).toBe("gemma 4 e4b fails on vertex");
+    expect(normalizeTitle("[Feature]:   ")).toBe("");
+  });
+});
+
+describe("candidateNumbers", () => {
+  test("reads only the marker field, keeps older issues, sorted ascending and deduplicated", () => {
+    const body = "<!-- litellm:potential-duplicate candidates=40,10,30,10, -->\n- #1 - see #1 (100% similar)";
+    expect(candidateNumbers(body, 35)).toEqual([10, 30]);
+  });
+
+  test("returns nothing without the marker", () => {
+    expect(candidateNumbers("- #1 - looks like #1", 35)).toEqual([]);
+  });
+});
+
+describe("pendingNotice", () => {
+  test("waits out the grace period from the latest notice", () => {
+    const fresh = pendingNotice(issue(35, "t"), [notice([10], daysAgo(2.9))], config);
+    expect(fresh.kind).toBe("skip");
+    const aged = pendingNotice(issue(35, "t"), [notice([10], daysAgo(3.1))], config);
+    expect(aged.kind).toBe("pending");
+    const reposted = pendingNotice(
+      issue(35, "t"),
+      [notice([10], daysAgo(6)), notice([10], daysAgo(1), { id: 902 })],
+      config,
+    );
+    expect(reposted.kind).toBe("skip");
+  });
+
+  test("an objection posted before a re-posted notice still keeps the issue open", () => {
+    const verdict = pendingNotice(
+      issue(35, "t"),
+      [notice([10], daysAgo(10)), humanComment(daysAgo(7)), notice([10], daysAgo(4), { id: 902 })],
+      config,
+    );
+    expect(verdict).toEqual({ kind: "skip", reason: "someone replied after the notice" });
+  });
+
+  test("a zero-day grace period acts on the notice at once", () => {
+    const verdict = pendingNotice(issue(35, "t"), [notice([10], daysAgo(0.01))], { ...config, graceDays: 0 });
+    expect(verdict.kind).toBe("pending");
+  });
+
+  test("a human reply after the notice keeps the issue open, a bot reply does not", () => {
+    const human = pendingNotice(issue(35, "t"), [notice([10], daysAgo(5)), humanComment(daysAgo(4))], config);
+    expect(human).toEqual({ kind: "skip", reason: "someone replied after the notice" });
+    const bot = pendingNotice(
+      issue(35, "t"),
+      [notice([10], daysAgo(5)), { id: 903, body: "triage", created_at: daysAgo(4), user: { type: "Bot", login: "triage[bot]" } }],
+      config,
+    );
+    expect(bot.kind).toBe("pending");
+  });
+
+  test("a human quoting the marker is not a notice", () => {
+    const quoted = pendingNotice(issue(35, "t"), [notice([10], daysAgo(5), { user: { type: "User", login: "reporter" } })], config);
+    expect(quoted).toEqual({ kind: "skip", reason: "carries no duplicate notice" });
+  });
+
+  test("never closes an issue twice: a reopened issue is left alone", () => {
+    const reopened = pendingNotice(
+      issue(35, "t"),
+      [notice([10], daysAgo(9)), { id: 904, body: `Closed automatically\n\n${CLOSED_MARKER}`, created_at: daysAgo(5), user: { type: "Bot", login: "github-actions[bot]" } }],
+      config,
+    );
+    expect(reopened).toEqual({ kind: "skip", reason: "was reopened after an automatic close" });
+  });
+
+  test("skips pull requests and issues whose only candidates are newer", () => {
+    expect(pendingNotice(issue(35, "t", { pull_request: {} }), [notice([10], daysAgo(5))], config).kind).toBe("skip");
+    expect(pendingNotice(issue(35, "t"), [notice([40], daysAgo(5))], config)).toEqual({
+      kind: "skip",
+      reason: "no candidate is older than this issue",
+    });
+  });
+});
+
+describe("duplicateTarget", () => {
+  const reporter = issue(35, "[Bug]: Gemma 4-e4b fails on Vertex");
+
+  test("closes only against the earliest open issue with the identical normalized title", () => {
+    const verdict = duplicateTarget(
+      reporter,
+      [issue(10, "[Bug]: Gemma 4-e4n fails on Vertex"), issue(20, "[bug]: gemma 4-e4b fails on vertex"), issue(30, "[Bug]: Gemma 4-e4b fails on Vertex")],
+      [],
+    );
+    expect(verdict).toEqual({ kind: "close", duplicateOf: 20 });
+  });
+
+  test("a near miss in the title is not a duplicate", () => {
+    const verdict = duplicateTarget(reporter, [issue(10, "[Bug]: Gemma 4-e4n fails on Vertex")], []);
+    expect(verdict).toEqual({ kind: "skip", reason: "no older open issue has the identical title" });
+  });
+
+  test("bare template titles never match each other", () => {
+    const verdict = duplicateTarget(issue(35, "[Bug]: "), [issue(10, "[Bug]: ")], []);
+    expect(verdict.kind).toBe("skip");
+    expect(verdict.kind === "skip" && verdict.reason).toContain("too short");
+  });
+
+  test("a closed candidate or a pull request is never the target", () => {
+    expect(duplicateTarget(reporter, [issue(10, reporter.title, { state: "closed" })], []).kind).toBe("skip");
+    expect(duplicateTarget(reporter, [issue(10, reporter.title, { pull_request: {} })], []).kind).toBe("skip");
+  });
+
+  test("a thumbs down on the notice keeps the issue open", () => {
+    const verdict = duplicateTarget(reporter, [issue(10, reporter.title)], [{ content: "+1" }, { content: "-1" }]);
+    expect(verdict).toEqual({ kind: "skip", reason: "someone gave the notice a thumbs down" });
+  });
+});
+
+describe("sweepIssue", () => {
+  const reporter = issue(35, "[Bug]: Gemma 4-e4b fails on Vertex");
+  const original = issue(10, "[Bug]: Gemma 4-e4b fails on Vertex");
+
+  function fakeApi(
+    comments: readonly Comment[] = [notice([10], daysAgo(5))],
+    reactionsByNotice: Readonly<Record<number, readonly Reaction[]>> = {},
+  ): { readonly api: GitHubApi; readonly writes: readonly string[] } {
+    const writes: string[] = [];
+    const api: GitHubApi = {
+      request: async <T>(method: string, path: string, body?: object): Promise<T> => {
+        if (method !== "GET") {
+          writes.push(`${method} ${path} ${JSON.stringify(body)}`);
+          return {} as T;
+        }
+        if (path.startsWith("/repos/BerriAI/litellm/issues/35/comments")) {
+          return comments as T;
+        }
+        const reactionsPath = path.match(/^\/repos\/BerriAI\/litellm\/issues\/comments\/(\d+)\/reactions/);
+        if (reactionsPath) {
+          return (reactionsByNotice[Number(reactionsPath[1])] ?? []) as T;
+        }
+        if (path === "/repos/BerriAI/litellm/issues/10") {
+          return original as T;
+        }
+        throw new Error(`unexpected GET ${path}`);
+      },
+    };
+    return { api, writes };
+  }
+
+  test("a dry run reports the close and writes nothing", async () => {
+    const { api, writes } = fakeApi();
+    const verdict = await sweepIssue(api, { ...config, dryRun: true }, reporter);
+    expect(verdict).toEqual({ kind: "close", duplicateOf: 10 });
+    expect(writes).toEqual([]);
+  });
+
+  test("a thumbs down on an earlier notice still keeps the issue open", async () => {
+    const { api, writes } = fakeApi([notice([10], daysAgo(9)), notice([10], daysAgo(5), { id: 902 })], { 900: [{ content: "-1" }] });
+    const verdict = await sweepIssue(api, config, reporter);
+    expect(verdict).toEqual({ kind: "skip", reason: "someone gave the notice a thumbs down" });
+    expect(writes).toEqual([]);
+  });
+
+  test("a real run comments, labels, then closes with the duplicate reason", async () => {
+    const { api, writes } = fakeApi();
+    const verdict = await sweepIssue(api, config, reporter);
+    expect(verdict).toEqual({ kind: "close", duplicateOf: 10 });
+    expect(writes.map((write) => write.split(" ").slice(0, 2).join(" "))).toEqual([
+      "POST /repos/BerriAI/litellm/issues/35/comments",
+      "POST /repos/BerriAI/litellm/issues/35/labels",
+      "PATCH /repos/BerriAI/litellm/issues/35",
+    ]);
+    expect(writes[0]).toContain("duplicate of #10");
+    expect(writes[0]).toContain("unanswered for 3 days");
+    expect(writes[0]).toContain(CLOSED_MARKER);
+    expect(writes[1]).toContain('{"labels":["duplicate"]}');
+    expect(writes[2]).toContain('{"state":"closed","state_reason":"duplicate"}');
+  });
+});
+
+describe("reopenTarget", () => {
+  const closedByBot = (overrides: Partial<Issue> = {}): Issue =>
+    issue(35, "t", { state: "closed", closed_by: { type: "Bot" }, ...overrides });
+  const closeMarker = (createdAt: string): Comment => ({
+    id: 905,
+    body: `Closed automatically as a duplicate of #10.\n\n${CLOSED_MARKER}`,
+    created_at: createdAt,
+    user: { type: "Bot", login: "github-actions[bot]" },
+  });
+
+  test("a reporter reply after the automatic close reopens", () => {
+    const verdict = reopenTarget(closedByBot(), [closeMarker(daysAgo(2)), humanComment(daysAgo(1))]);
+    expect(verdict).toEqual({ kind: "reopen" });
+  });
+
+  test("an issue closed by a person stays closed", () => {
+    const verdict = reopenTarget(closedByBot({ closed_by: { type: "User" } }), [
+      closeMarker(daysAgo(2)),
+      humanComment(daysAgo(1)),
+    ]);
+    expect(verdict).toEqual({ kind: "skip", reason: "was closed by a person" });
+  });
+
+  test("without the automatic-close marker nothing reopens", () => {
+    const verdict = reopenTarget(closedByBot(), [humanComment(daysAgo(1))]);
+    expect(verdict).toEqual({ kind: "skip", reason: "carries no automatic-close marker" });
+  });
+
+  test("a maintainer reply alone does not reopen", () => {
+    const verdict = reopenTarget(closedByBot(), [
+      closeMarker(daysAgo(2)),
+      humanComment(daysAgo(1), "Confirmed duplicate", "maintainer"),
+    ]);
+    expect(verdict).toEqual({ kind: "skip", reason: "the reporter has not replied since the close" });
+  });
+
+  test("a reporter comment from before the close does not reopen", () => {
+    const verdict = reopenTarget(closedByBot(), [humanComment(daysAgo(3)), closeMarker(daysAgo(2))]);
+    expect(verdict).toEqual({ kind: "skip", reason: "the reporter has not replied since the close" });
+  });
+
+  test("a pull request never reopens", () => {
+    const verdict = reopenTarget(closedByBot({ pull_request: {} }), [closeMarker(daysAgo(2)), humanComment(daysAgo(1))]);
+    expect(verdict).toEqual({ kind: "skip", reason: "is a pull request" });
+  });
+});
+
+describe("sweepClosedIssue", () => {
+  function fakeApi(issueBody: Issue, comments: readonly Comment[]): { readonly api: GitHubApi; readonly writes: readonly string[] } {
+    const writes: string[] = [];
+    const api: GitHubApi = {
+      request: async <T>(method: string, path: string, body?: object): Promise<T> => {
+        if (method !== "GET") {
+          writes.push(`${method} ${path} ${JSON.stringify(body)}`);
+          return {} as T;
+        }
+        if (path.startsWith("/repos/BerriAI/litellm/issues/35/comments")) {
+          return comments as T;
+        }
+        if (path === "/repos/BerriAI/litellm/issues/35") {
+          return issueBody as T;
+        }
+        throw new Error(`unexpected GET ${path}`);
+      },
+    };
+    return { api, writes };
+  }
+
+  const closedByBot = issue(35, "t", { state: "closed", closed_by: { type: "Bot" } });
+  const closeMarker: Comment = {
+    id: 905,
+    body: `Closed automatically as a duplicate of #10.\n\n${CLOSED_MARKER}`,
+    created_at: daysAgo(2),
+    user: { type: "Bot", login: "github-actions[bot]" },
+  };
+
+  test("a real run unlabels, reopens, then explains", async () => {
+    const { api, writes } = fakeApi(closedByBot, [closeMarker, humanComment(daysAgo(1))]);
+    const verdict = await sweepClosedIssue(api, config, 35);
+    expect(verdict).toEqual({ kind: "reopen" });
+    expect(writes).toEqual([
+      "DELETE /repos/BerriAI/litellm/issues/35/labels/duplicate undefined",
+      'PATCH /repos/BerriAI/litellm/issues/35 {"state":"open"}',
+      `POST /repos/BerriAI/litellm/issues/35/comments {"body":"${REOPEN_COMMENT}"}`,
+    ]);
+  });
+
+  test("a dry run reports the reopen and writes nothing", async () => {
+    const { api, writes } = fakeApi(closedByBot, [closeMarker, humanComment(daysAgo(1))]);
+    const verdict = await sweepClosedIssue(api, { ...config, dryRun: true }, 35);
+    expect(verdict).toEqual({ kind: "reopen" });
+    expect(writes).toEqual([]);
+  });
+});
+
+describe("readConfig", () => {
+  test("defaults to a real run with a 3-day grace period", () => {
+    const parsed = readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "BerriAI/litellm" }, NOW);
+    expect(parsed).toEqual({ token: "t", repo: "BerriAI/litellm", graceDays: 3, dryRun: false, now: NOW });
+  });
+
+  test("honors DRY_RUN and GRACE_PERIOD_DAYS overrides", () => {
+    const parsed = readConfig(
+      { GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", DRY_RUN: "true", GRACE_PERIOD_DAYS: "0" },
+      NOW,
+    );
+    expect(parsed.dryRun).toBe(true);
+    expect(parsed.graceDays).toBe(0);
+  });
+
+  test("an empty GRACE_PERIOD_DAYS, as a schedule run renders it, means the default", () => {
+    const parsed = readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", GRACE_PERIOD_DAYS: "" }, NOW);
+    expect(parsed.graceDays).toBe(3);
+  });
+
+  test("refuses a missing token, a malformed repository, or a bad grace period", () => {
+    expect(() => readConfig({ GITHUB_REPOSITORY: "o/r" }, NOW)).toThrow("GITHUB_TOKEN");
+    expect(() => readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "litellm" }, NOW)).toThrow("owner/repo");
+    expect(() => readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", GRACE_PERIOD_DAYS: "-1" }, NOW)).toThrow(
+      "GRACE_PERIOD_DAYS",
+    );
+  });
+});
diff --git a/scripts/auto-close-duplicates.ts b/scripts/auto-close-duplicates.ts
new file mode 100644
index 00000000000..c595104d886
--- /dev/null
+++ b/scripts/auto-close-duplicates.ts
@@ -0,0 +1,300 @@
+#!/usr/bin/env bun
+
+declare const process: { readonly env: Readonly<Record<string, string | undefined>> };
+
+export interface Issue {
+  readonly number: number;
+  readonly title: string;
+  readonly state: string;
+  readonly user: { readonly login: string };
+  readonly closed_by?: { readonly type: string } | null;
+  readonly pull_request?: unknown;
+}
+
+export interface Comment {
+  readonly id: number;
+  readonly body: string;
+  readonly created_at: string;
+  readonly user: { readonly type: string; readonly login: string };
+}
+
+export interface Reaction {
+  readonly content: string;
+}
+
+export interface GitHubApi {
+  readonly request: <T>(method: "GET" | "POST" | "PATCH" | "DELETE", path: string, body?: object) => Promise<T>;
+}
+
+export interface SweepConfig {
+  readonly repo: string;
+  readonly graceDays: number;
+  readonly dryRun: boolean;
+  readonly now: Date;
+}
+
+export type NoticeVerdict =
+  | { readonly kind: "pending"; readonly notices: readonly Comment[]; readonly candidates: readonly number[] }
+  | { readonly kind: "skip"; readonly reason: string };
+
+export type CloseVerdict =
+  | { readonly kind: "close"; readonly duplicateOf: number }
+  | { readonly kind: "skip"; readonly reason: string };
+
+export type ReopenVerdict =
+  | { readonly kind: "reopen" }
+  | { readonly kind: "skip"; readonly reason: string };
+
+export const FLAG_LABEL = "potential-duplicate";
+export const CLOSED_MARKER = "<!-- litellm:closed-as-duplicate -->";
+export const DEFAULT_GRACE_DAYS = 3;
+export const REOPEN_COMMENT =
+  "Reopened automatically: the reporter replied after the duplicate close, so this needs a human look.";
+const NOTICE_MARKER = /<!-- litellm:potential-duplicate candidates=([\d,]*) -->/;
+const MIN_TITLE_WORDS = 3;
+const PAGE_SIZE = 100;
+const DAY_MS = 24 * 60 * 60 * 1000;
+const REOPEN_LOOKBACK_DAYS = 30;
+
+const skip = (reason: string): { readonly kind: "skip"; readonly reason: string } => ({ kind: "skip", reason });
+
+export function normalizeTitle(title: string): string {
+  return title
+    .toLowerCase()
+    .replace(/^\s*\[[^\]]*\]\s*:?/, "")
+    .replace(/[^a-z0-9]+/g, " ")
+    .trim();
+}
+
+export function candidateNumbers(noticeBody: string, issueNumber: number): readonly number[] {
+  const field = noticeBody.match(NOTICE_MARKER);
+  if (!field) {
+    return [];
+  }
+  const older = field[1]
+    .split(",")
+    .filter((value) => value !== "")
+    .map(Number)
+    .filter((candidate) => candidate < issueNumber);
+  return [...new Set(older)].sort((a, b) => a - b);
+}
+
+export function pendingNotice(
+  issue: Issue,
+  comments: readonly Comment[],
+  config: Pick<SweepConfig, "graceDays" | "now">,
+): NoticeVerdict {
+  if (issue.pull_request !== undefined) {
+    return skip("is a pull request");
+  }
+  if (comments.some((comment) => comment.body.includes(CLOSED_MARKER))) {
+    return skip("was reopened after an automatic close");
+  }
+  const notices = comments.filter((comment) => comment.user.type === "Bot" && NOTICE_MARKER.test(comment.body));
+  const first = notices[0];
+  const latest = notices[notices.length - 1];
+  if (first === undefined || latest === undefined) {
+    return skip("carries no duplicate notice");
+  }
+  const ageDays = (config.now.getTime() - new Date(latest.created_at).getTime()) / DAY_MS;
+  if (ageDays < config.graceDays) {
+    return skip(`notice is ${ageDays.toFixed(1)} days old, grace period is ${config.graceDays}`);
+  }
+  const firstNoticeAt = new Date(first.created_at);
+  if (comments.some((comment) => comment.user.type !== "Bot" && new Date(comment.created_at) > firstNoticeAt)) {
+    return skip("someone replied after the notice");
+  }
+  const candidates = candidateNumbers(latest.body, issue.number);
+  if (candidates.length === 0) {
+    return skip("no candidate is older than this issue");
+  }
+  return { kind: "pending", notices, candidates };
+}
+
+export function duplicateTarget(
+  issue: Issue,
+  candidates: readonly Issue[],
+  reactions: readonly Reaction[],
+): CloseVerdict {
+  if (reactions.some((reaction) => reaction.content === "-1")) {
+    return skip("someone gave the notice a thumbs down");
+  }
+  const title = normalizeTitle(issue.title);
+  if (title.split(" ").length < MIN_TITLE_WORDS) {
+    return skip(`title "${issue.title}" is too short to match on`);
+  }
+  const original = candidates.find(
+    (candidate) =>
+      candidate.state === "open" && candidate.pull_request === undefined && normalizeTitle(candidate.title) === title,
+  );
+  if (original === undefined) {
+    return skip("no older open issue has the identical title");
+  }
+  return { kind: "close", duplicateOf: original.number };
+}
+
+export function reopenTarget(issue: Issue, comments: readonly Comment[]): ReopenVerdict {
+  if (issue.pull_request !== undefined) {
+    return skip("is a pull request");
+  }
+  if (issue.closed_by?.type !== "Bot") {
+    return skip("was closed by a person");
+  }
+  const marker = comments.find((comment) => comment.body.includes(CLOSED_MARKER));
+  if (marker === undefined) {
+    return skip("carries no automatic-close marker");
+  }
+  const markerAt = new Date(marker.created_at);
+  if (!comments.some((comment) => comment.user.login === issue.user.login && new Date(comment.created_at) > markerAt)) {
+    return skip("the reporter has not replied since the close");
+  }
+  return { kind: "reopen" };
+}
+
+export function closingComment(duplicateOf: number, graceDays: number): string {
+  return `Closed automatically as a duplicate of #${duplicateOf}. Its title is identical to that older open issue and the duplicate notice above went unanswered for ${graceDays} days. If this is wrong, comment here with how it differs from #${duplicateOf} and this issue will be reopened automatically within a day.
+
+${CLOSED_MARKER}`;
+}
+
+async function listAll<T>(api: GitHubApi, path: string, page = 1): Promise<readonly T[]> {
+  const separator = path.includes("?") ? "&" : "?";
+  const batch = await api.request<readonly T[]>("GET", `${path}${separator}per_page=${PAGE_SIZE}&page=${page}`);
+  return batch.length < PAGE_SIZE ? batch : [...batch, ...(await listAll<T>(api, path, page + 1))];
+}
+
+async function closeAsDuplicate(
+  api: GitHubApi,
+  config: SweepConfig,
+  issueNumber: number,
+  duplicateOf: number,
+): Promise<void> {
+  const issuePath = `/repos/${config.repo}/issues/${issueNumber}`;
+  await api.request("POST", `${issuePath}/comments`, { body: closingComment(duplicateOf, config.graceDays) });
+  await api.request("POST", `${issuePath}/labels`, { labels: ["duplicate"] });
+  await api.request("PATCH", issuePath, { state: "closed", state_reason: "duplicate" });
+}
+
+async function reopenForReporter(api: GitHubApi, config: SweepConfig, issueNumber: number): Promise<void> {
+  const issuePath = `/repos/${config.repo}/issues/${issueNumber}`;
+  await api.request("DELETE", `${issuePath}/labels/duplicate`);
+  await api.request("PATCH", issuePath, { state: "open" });
+  await api.request("POST", `${issuePath}/comments`, { body: REOPEN_COMMENT });
+}
+
+export async function sweepClosedIssue(api: GitHubApi, config: SweepConfig, issueNumber: number): Promise<ReopenVerdict> {
+  const issue = await api.request<Issue>("GET", `/repos/${config.repo}/issues/${issueNumber}`);
+  const comments = await listAll<Comment>(api, `/repos/${config.repo}/issues/${issueNumber}/comments`);
+  const verdict = reopenTarget(issue, comments);
+  if (verdict.kind === "reopen" && !config.dryRun) {
+    await reopenForReporter(api, config, issueNumber);
+  }
+  return verdict;
+}
+
+export async function sweepIssue(api: GitHubApi, config: SweepConfig, issue: Issue): Promise<CloseVerdict> {
+  const comments = await listAll<Comment>(api, `/repos/${config.repo}/issues/${issue.number}/comments`);
+  const pending = pendingNotice(issue, comments, config);
+  if (pending.kind === "skip") {
+    return pending;
+  }
+  const reactions = (
+    await Promise.all(
+      pending.notices.map((notice) => listAll<Reaction>(api, `/repos/${config.repo}/issues/comments/${notice.id}/reactions`)),
+    )
+  ).flat();
+  const candidates = await Promise.all(
+    pending.candidates.map((candidate) => api.request<Issue>("GET", `/repos/${config.repo}/issues/${candidate}`)),
+  );
+  const verdict = duplicateTarget(issue, candidates, reactions);
+  if (verdict.kind === "close" && !config.dryRun) {
+    await closeAsDuplicate(api, config, issue.number, verdict.duplicateOf);
+  }
+  return verdict;
+}
+
+function describe(issue: Issue, verdict: CloseVerdict, dryRun: boolean): string {
+  if (verdict.kind === "skip") {
+    return `#${issue.number}: skipped, ${verdict.reason}`;
+  }
+  return `#${issue.number}: ${dryRun ? "would close" : "closed"} as a duplicate of #${verdict.duplicateOf}`;
+}
+
+export async function sweep(api: GitHubApi, config: SweepConfig): Promise<readonly CloseVerdict[]> {
+  const issues = await listAll<Issue>(api, `/repos/${config.repo}/issues?state=open&labels=${FLAG_LABEL}`);
+  console.log(`${issues.length} open issues carry the ${FLAG_LABEL} label in ${config.repo}${config.dryRun ? " (dry run)" : ""}`);
+  return issues.reduce<Promise<readonly CloseVerdict[]>>(async (previous, issue) => {
+    const verdicts = await previous;
+    const verdict = await sweepIssue(api, config, issue);
+    console.log(describe(issue, verdict, config.dryRun));
+    return [...verdicts, verdict];
+  }, Promise.resolve([]));
+}
+
+function describeReopen(issueNumber: number, verdict: ReopenVerdict, dryRun: boolean): string {
+  if (verdict.kind === "skip") {
+    return `#${issueNumber}: skipped, ${verdict.reason}`;
+  }
+  return `#${issueNumber}: ${dryRun ? "would reopen" : "reopened"} for the reporter's reply`;
+}
+
+export async function reopenSweep(api: GitHubApi, config: SweepConfig): Promise<readonly ReopenVerdict[]> {
+  const since = new Date(config.now.getTime() - REOPEN_LOOKBACK_DAYS * DAY_MS).toISOString();
+  const closedPath = `/repos/${config.repo}/issues?state=closed&labels=duplicate,${FLAG_LABEL}&since=${encodeURIComponent(since)}`;
+  const issues = await listAll<Issue>(api, closedPath);
+  console.log(`${issues.length} recently closed issues carry the duplicate and ${FLAG_LABEL} labels in ${config.repo}${config.dryRun ? " (dry run)" : ""}`);
+  return issues.reduce<Promise<readonly ReopenVerdict[]>>(async (previous, issue) => {
+    const verdicts = await previous;
+    const verdict = await sweepClosedIssue(api, config, issue.number);
+    console.log(describeReopen(issue.number, verdict, config.dryRun));
+    return [...verdicts, verdict];
+  }, Promise.resolve([]));
+}
+
+export function readConfig(env: Readonly<Record<string, string | undefined>>, now: Date): SweepConfig & { readonly token: string } {
+  const token = env.GITHUB_TOKEN;
+  const repo = env.GITHUB_REPOSITORY;
+  if (!token || !repo || !/^[\w.-]+\/[\w.-]+$/.test(repo)) {
+    throw new Error("GITHUB_TOKEN and GITHUB_REPOSITORY (owner/repo) are required");
+  }
+  const rawGraceDays = env.GRACE_PERIOD_DAYS?.trim();
+  const graceDays = rawGraceDays === undefined || rawGraceDays === "" ? DEFAULT_GRACE_DAYS : Number(rawGraceDays);
+  if (!Number.isFinite(graceDays) || graceDays < 0) {
+    throw new Error(`GRACE_PERIOD_DAYS must be a non-negative number, got "${env.GRACE_PERIOD_DAYS}"`);
+  }
+  return { token, repo, graceDays, dryRun: env.DRY_RUN === "true", now };
+}
+
+export function githubApi(token: string): GitHubApi {
+  return {
+    request: async <T>(method: "GET" | "POST" | "PATCH" | "DELETE", path: string, body?: object): Promise<T> => {
+      const response = await fetch(`https://api.github.com${path}`, {
+        method,
+        headers: {
+          Authorization: `Bearer ${token}`,
+          Accept: "application/vnd.github+json",
+          "X-GitHub-Api-Version": "2022-11-28",
+          "User-Agent": "litellm-auto-close-duplicates",
+          ...(body === undefined ? {} : { "Content-Type": "application/json" }),
+        },
+        body: body === undefined ? undefined : JSON.stringify(body),
+      });
+      if (!response.ok) {
+        throw new Error(`${method} ${path} failed: ${response.status} ${response.statusText}`);
+      }
+      return (await response.json()) as T;
+    },
+  };
+}
+
+if (import.meta.main) {
+  const { token, ...config } = readConfig(process.env, new Date());
+  const api = githubApi(token);
+  const closeVerdicts = await sweep(api, config);
+  const reopenVerdicts = await reopenSweep(api, config);
+  const closed = closeVerdicts.filter((verdict) => verdict.kind === "close").length;
+  const reopened = reopenVerdicts.filter((verdict) => verdict.kind === "reopen").length;
+  console.log(
+    `${config.dryRun ? "Would close" : "Closed"} ${closed} of ${closeVerdicts.length} flagged issues, ${config.dryRun ? "would reopen" : "reopened"} ${reopened}`,
+  );
+}
diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py
index 3bde72ccd49..35de9961054 100644
--- a/tests/proxy_unit_tests/test_proxy_utils.py
+++ b/tests/proxy_unit_tests/test_proxy_utils.py
@@ -1317,6 +1317,62 @@ def test_proxy_config_state_post_init_callback_call(monkeypatch):
     assert config["litellm_settings"]["default_team_settings"][0]["team_id"] == "test"
 
 
+@pytest.mark.asyncio
+async def test_default_team_settings_newrelic_resolves_traces_and_metrics():
+    """Static `default_team_settings` is the config-file twin of POST /team/callback.
+
+    A team pinned to New Relic through `default_team_settings` must reach the
+    same two loggers the dynamic path does: the per-team metrics logger (cost
+    and usage) and the trace logger (LLM/agent spans). This proves the static
+    path resolves both, not just one, so the config-file customer gets the
+    same per-team routing as the API customer.
+    """
+    from litellm.litellm_core_utils.litellm_logging import Logging
+    from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    pc = ProxyConfig()
+    pc.config = {
+        "litellm_settings": {
+            "default_team_settings": [
+                {
+                    "team_id": "team-a",
+                    "success_callback": ["newrelic"],
+                    "newrelic_api_key": "team-a-ingest-key",
+                    "newrelic_region": "eu",
+                }
+            ]
+        }
+    }
+
+    callback_metadata = LiteLLMProxyRequestSetup.add_team_based_callbacks_from_config(
+        team_id="team-a",
+        proxy_config=pc,
+    )
+
+    assert callback_metadata is not None
+    assert callback_metadata.success_callback == ["newrelic"]
+    assert callback_metadata.callback_vars == {
+        "newrelic_api_key": "team-a-ingest-key",
+        "newrelic_region": "eu",
+    }
+
+    logging_obj = Logging(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=False,
+        call_type="completion",
+        start_time=None,
+        litellm_call_id="static-nr-1",
+        function_id="static-nr-1",
+    )
+    logging_obj._trusted_callback_vars = tuple(callback_metadata.callback_vars.items())
+
+    resolved = logging_obj._resolve_dynamic_callback_string("newrelic")
+    resolved_names = {type(logger).__name__ for logger in resolved}
+    assert resolved_names == {"NewRelicMetricsLogger", "NewRelicLogger"}
+
+
 def test_proxy_config_state_get_config_state_error():
     """
     Ensures that get_config_state does not raise an error when the config is not a valid dictionary
diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_components.py b/tests/test_litellm/integrations/otel/test_otel_v2_components.py
index 115e385eda4..4aa28b5abfd 100644
--- a/tests/test_litellm/integrations/otel/test_otel_v2_components.py
+++ b/tests/test_litellm/integrations/otel/test_otel_v2_components.py
@@ -3,6 +3,7 @@ baggage helpers, metrics, the typed coercion helpers, mapper branches, span-name
 builders, and the registry validator's failure paths. Needs the OTel SDK."""
 
 import json
+from dataclasses import replace
 
 import pytest
 
@@ -215,6 +216,27 @@ def test_genai_mapper_all_request_params():
     assert attrs["server.port"] == 443
 
 
+def test_genai_mapper_cache_token_attrs():
+    cached = replace(
+        _full_llm_call(),
+        usage=LLMUsage(
+            input_tokens=10,
+            output_tokens=5,
+            total_tokens=15,
+            cache_creation_input_tokens=7,
+            cache_read_input_tokens=3,
+        ),
+    )
+    attrs = GenAIMapper().map(cached)
+    assert attrs[GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS] == 7
+    assert attrs[GenAI.USAGE_CACHE_READ_INPUT_TOKENS] == 3
+
+    # No cache usage keeps the span sparse: neither key present.
+    uncached = GenAIMapper().map(_full_llm_call())
+    assert GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS not in uncached
+    assert GenAI.USAGE_CACHE_READ_INPUT_TOKENS not in uncached
+
+
 def test_genai_mapper_stamps_input_output_messages():
     data = LLMCallSpanData(
         operation=GenAIOperation.CHAT,
diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py
index baa72b5a7fe..ca628aa3405 100644
--- a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py
+++ b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py
@@ -525,6 +525,28 @@ def test_llm_call_adapter_extracts_all_fields():
     assert data.identity.key_hash == "hsh"
 
 
+def test_llm_call_adapter_extracts_cache_tokens_from_usage_object():
+    payload = _sample_payload()
+    payload["metadata"] = {
+        **payload["metadata"],
+        "usage_object": {
+            "prompt_tokens": 10,
+            "completion_tokens": 5,
+            "cache_creation_input_tokens": 7,
+            "cache_read_input_tokens": 3,
+        },
+    }
+    data = LLMCallSpanData.from_standard_logging_payload(payload)
+    assert data.usage.cache_creation_input_tokens == 7
+    assert data.usage.cache_read_input_tokens == 3
+
+
+def test_llm_call_adapter_cache_tokens_none_without_usage_object():
+    data = LLMCallSpanData.from_standard_logging_payload(_sample_payload())
+    assert data.usage.cache_creation_input_tokens is None
+    assert data.usage.cache_read_input_tokens is None
+
+
 def test_llm_call_adapter_failure_path():
     payload = _sample_payload(
         status="failure",
diff --git a/tests/test_litellm/litellm_core_utils/test_token_counter.py b/tests/test_litellm/litellm_core_utils/test_token_counter.py
index 572b505e94c..4694fa8fbed 100644
--- a/tests/test_litellm/litellm_core_utils/test_token_counter.py
+++ b/tests/test_litellm/litellm_core_utils/test_token_counter.py
@@ -1377,3 +1377,38 @@ def test_anthropic_document_title_and_context_add_their_tokens():
             {"type": "document", "source": source},
         ]
     )
+
+
+def test_openai_file_block_prices_like_the_equivalent_anthropic_document():
+    """An inline `file` is a `document` in the chat-completions dialect, so it must price identically, not raise.
+
+    Before the fix `file` was missing from the content-block match even though `ChatCompletionFileObject`
+    is in the union this counter accepts, so every local count of a Responses `input_file` raised
+    `Invalid content item type: file` and surfaced as a 500 on /v1/responses/input_tokens.
+    """
+    prompt = {"type": "text", "text": "Summarize this file."}
+    inline_file = {
+        "type": "file",
+        "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"},
+    }
+    document = {
+        "type": "document",
+        "title": "report.pdf",
+        "source": {"type": "base64", "media_type": "application/pdf", "data": "JVBERi0xLjQK"},
+    }
+
+    assert _count_user_content([prompt, inline_file]) == _count_user_content([prompt, document])
+    assert _count_user_content([prompt, inline_file]) > _count_user_content([prompt])
+
+
+def test_openai_file_block_without_inline_bytes_counts_what_it_carries():
+    """A `file` block naming an uploaded file has no bytes to price, so it adds only the filename's tokens."""
+    prompt = {"type": "text", "text": "Summarize this file."}
+
+    by_id = {"type": "file", "file": {"file_id": "file-abc123"}}
+    assert _count_user_content([prompt, by_id]) == _count_user_content([prompt])
+
+    named = {"type": "file", "file": {"file_id": "file-abc123", "filename": "report.pdf"}}
+    assert _count_user_content([prompt, named]) == _count_user_content(
+        [prompt, {"type": "text", "text": "report.pdf"}]
+    )
diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index 226bba6826a..63f895e1819 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -957,6 +957,28 @@ def test_transform_request_helper_includes_anthropic_beta_and_tools():
     assert fields["tools"][0]["type"] == "computer_20250124"
 
 
+def test_config_blocks_do_not_leak_into_inference_config():
+    """Regression: inferenceConfig was built before the config blocks were popped, so a dead
+    nested copy of each block (guardrailConfig, performanceConfig, serviceTier) rode inside
+    inferenceConfig alongside the real top-level one."""
+    data = AmazonConverseConfig()._transform_request_helper(
+        model="anthropic.claude-haiku-4-5-20251001-v1:0",
+        system_content_blocks=[],
+        optional_params={
+            "maxTokens": 100,
+            "guardrailConfig": {"guardrailIdentifier": "gr-id", "guardrailVersion": "DRAFT"},
+            "performanceConfig": {"latency": "optimized"},
+            "serviceTier": {"type": "priority"},
+        },
+        messages=[{"role": "user", "content": "hi"}],
+    )
+
+    assert data["inferenceConfig"] == {"maxTokens": 100}
+    assert data["guardrailConfig"] == {"guardrailIdentifier": "gr-id", "guardrailVersion": "DRAFT"}
+    assert data["performanceConfig"] == {"latency": "optimized"}
+    assert data["serviceTier"] == {"type": "priority"}
+
+
 def test_parallel_tool_calls_config_kept_for_sonnet_5(monkeypatch):
     old_env = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP")
     old_cost = litellm.model_cost
@@ -2853,17 +2875,11 @@ def test_guarded_text_guardrail_config_preserved():
         headers={},
     )
 
-    # GuardrailConfig should be present at top level
     assert "guardrailConfig" in result
     assert result["guardrailConfig"]["guardrailIdentifier"] == "gr-abc123"
 
-    # GuardrailConfig should also be in inferenceConfig
     assert "inferenceConfig" in result
-    assert "guardrailConfig" in result["inferenceConfig"]
-    assert (
-        result["inferenceConfig"]["guardrailConfig"]["guardrailIdentifier"]
-        == "gr-abc123"
-    )
+    assert "guardrailConfig" not in result["inferenceConfig"]
 
 
 def test_auto_convert_last_user_message_to_guarded_text():
diff --git a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py
index e1cc6a92927..c2efc1acdb9 100644
--- a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py
+++ b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py
@@ -163,6 +163,240 @@ def test_messages_to_responses_input_with_tool():
     }
 
 
+def test_messages_to_responses_input_preserves_images():
+    """An image block must survive the round trip, or OpenAI counts only the text.
+
+    A 256x256 image is worth 255 tokens to OpenAI's counting API; dropping it
+    turned a 268-token request into a 13-token one.
+    """
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What is in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "data:image/png;base64,iVBORw0KGgo=", "detail": "high"},
+                },
+            ],
+        }
+    ]
+
+    input_items, instructions = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert instructions is None
+    assert input_items == [
+        {
+            "role": "user",
+            "content": (
+                {"type": "input_text", "text": "What is in this image?"},
+                {
+                    "type": "input_image",
+                    "image_url": "data:image/png;base64,iVBORw0KGgo=",
+                    "detail": "high",
+                },
+            ),
+        }
+    ]
+
+
+def test_messages_to_responses_input_image_without_detail_defaults_to_auto():
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items[0]["content"] == (
+        {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"},
+    )
+
+
+def test_messages_to_responses_input_bare_string_image_url_is_preserved():
+    messages = [{"role": "user", "content": [{"type": "image_url", "image_url": "https://example.com/cat.png"}]}]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items[0]["content"] == (
+        {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"},
+    )
+
+
+def test_messages_to_responses_input_text_only_blocks_stay_a_joined_string():
+    """Text-only content must keep collapsing to a string so existing counts do not shift."""
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "text", "text": "first"}, {"type": "text", "text": "second"}],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [{"role": "user", "content": "first\nsecond"}]
+
+
+def test_messages_to_responses_input_drops_unmappable_blocks():
+    """A block with no Responses API equivalent is skipped, never forwarded verbatim."""
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "hi"},
+                {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}},
+                {"type": "input_audio", "input_audio": {"data": "AAAA", "format": "wav"}},
+            ],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items[0]["content"] == (
+        {"type": "input_text", "text": "hi"},
+        {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"},
+    )
+
+
+def test_messages_to_responses_input_assistant_blocks_collapse_to_a_string():
+    """An assistant turn must never forward chat `text` blocks.
+
+    The Responses API only accepts output_text and refusal inside an assistant turn, so
+    forwarding them 400s the whole request and silently drops the count back to the local
+    tokenizer, which is exactly what defeats the image fix above.
+    """
+    messages = [
+        {"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]},
+        {"role": "assistant", "content": [{"type": "text", "text": "Paris."}]},
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [
+        {"role": "user", "content": "What is the capital of France?"},
+        {"role": "assistant", "content": "Paris."},
+    ]
+
+
+def test_messages_to_responses_input_assistant_image_block_is_dropped():
+    """An image part is illegal inside an assistant turn, so it must not reach the provider."""
+    messages = [
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": "Here it is"},
+                {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}},
+            ],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [{"role": "assistant", "content": "Here it is"}]
+
+
+def test_messages_to_responses_input_keeps_user_image_alongside_an_assistant_turn():
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What is in this image?"},
+                {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}},
+            ],
+        },
+        {"role": "assistant", "content": [{"type": "text", "text": "A cat."}]},
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [
+        {
+            "role": "user",
+            "content": (
+                {"type": "input_text", "text": "What is in this image?"},
+                {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"},
+            ),
+        },
+        {"role": "assistant", "content": "A cat."},
+    ]
+
+
+def test_messages_to_responses_input_preserves_inline_files():
+    """An inline file must survive the round trip, or the count silently drops the file.
+
+    A small PDF is worth 36 tokens to OpenAI's counting API; dropping it left the same
+    request counting 13, the text-only total.
+    """
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Summarize this file."},
+                {
+                    "type": "file",
+                    "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="},
+                },
+            ],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [
+        {
+            "role": "user",
+            "content": (
+                {"type": "input_text", "text": "Summarize this file."},
+                {
+                    "type": "input_file",
+                    "filename": "report.pdf",
+                    "file_data": "data:application/pdf;base64,JVBERi0=",
+                },
+            ),
+        }
+    ]
+
+
+def test_messages_to_responses_input_drops_a_file_with_no_inline_data():
+    """OpenAI rejects `file_data` without a `filename`, and a rejected request loses the whole count."""
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Summarize this file."},
+                {"type": "file", "file": {"file_data": "data:application/pdf;base64,JVBERi0="}},
+                {"type": "file", "file": {"file_id": "file-abc123"}},
+            ],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [{"role": "user", "content": "Summarize this file."}]
+
+
+def test_messages_to_responses_input_assistant_file_block_is_dropped():
+    """A file part is illegal inside an assistant turn, so it must not reach the provider."""
+    messages = [
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": "Here it is"},
+                {
+                    "type": "file",
+                    "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="},
+                },
+            ],
+        }
+    ]
+
+    input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages)
+
+    assert input_items == [{"role": "assistant", "content": "Here it is"}]
+
+
 def test_validate_request_valid():
     """Test that valid requests pass validation."""
     config = OpenAICountTokensConfig()
diff --git a/tests/test_litellm/llms/openai/test_openai_workload_identity.py b/tests/test_litellm/llms/openai/test_openai_workload_identity.py
new file mode 100644
index 00000000000..d8d9936e9a1
--- /dev/null
+++ b/tests/test_litellm/llms/openai/test_openai_workload_identity.py
@@ -0,0 +1,238 @@
+import json
+import sys
+from pathlib import Path
+from typing import Final
+
+import httpx
+import pytest
+import respx
+from openai import AsyncOpenAI, OpenAI
+
+import litellm
+from litellm.llms.litellm_proxy.responses.transformation import LiteLLMProxyResponsesAPIConfig
+from litellm.llms.openai.common_utils import BaseOpenAILLM, OpenAIError
+from litellm.llms.openai.openai import OpenAIChatCompletion
+from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
+from litellm.llms.openai.workload_identity import (
+    OpenAIWorkloadIdentityConfig,
+    _workload_identity_auth,
+    get_workload_identity_bearer_token,
+    resolve_openai_workload_identity_config,
+)
+from litellm.types.router import GenericLiteLLMParams
+
+TOKEN_EXCHANGE_URL: Final = "https://auth.openai.com/oauth/token"
+
+
+@pytest.fixture
+def wif_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> OpenAIWorkloadIdentityConfig:
+    token_file: Final = tmp_path / "subject_token.jwt"
+    token_file.write_text("subject-token-from-file")
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
+    monkeypatch.delenv("OPENAI_API_BASE", raising=False)
+    monkeypatch.setattr(litellm, "api_base", None)
+    monkeypatch.setenv("OPENAI_IDENTITY_PROVIDER_ID", "idp_test123")
+    monkeypatch.setenv("OPENAI_SERVICE_ACCOUNT_ID", "user-test456")
+    monkeypatch.setenv("OPENAI_IDENTITY_TOKEN_FILE", str(token_file))
+    _workload_identity_auth.cache_clear()
+    litellm.in_memory_llm_clients_cache.flush_cache()
+    return OpenAIWorkloadIdentityConfig(
+        identity_provider_id="idp_test123",
+        service_account_id="user-test456",
+        token_file=str(token_file),
+    )
+
+
+def mock_token_exchange(access_token: str = "exchanged-bearer-token") -> respx.Route:
+    return respx.post(TOKEN_EXCHANGE_URL).mock(
+        return_value=httpx.Response(200, json={"access_token": access_token, "expires_in": 3600})
+    )
+
+
+class TestResolveConfig:
+    def test_resolves_from_env(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env
+
+    def test_static_api_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        assert resolve_openai_workload_identity_config(api_key="sk-static", api_base=None) is None
+
+    def test_env_openai_api_key_wins(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("OPENAI_API_KEY", "sk-from-env")
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None
+
+    @pytest.mark.parametrize("empty_key", ["", "   "])
+    def test_empty_api_key_arg_does_not_disable_wif(
+        self, wif_env: OpenAIWorkloadIdentityConfig, empty_key: str
+    ) -> None:
+        assert resolve_openai_workload_identity_config(api_key=empty_key, api_base=None) == wif_env
+
+    @pytest.mark.parametrize("empty_key", ["", "   "])
+    def test_empty_env_openai_api_key_does_not_disable_wif(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, empty_key: str
+    ) -> None:
+        monkeypatch.setenv("OPENAI_API_KEY", empty_key)
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env
+
+    def test_foreign_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        assert resolve_openai_workload_identity_config(api_key=None, api_base="https://my-vllm.internal/v1") is None
+
+    def test_openai_api_base_allows(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        assert resolve_openai_workload_identity_config(api_key=None, api_base="https://api.openai.com/v1") == wif_env
+
+    def test_plaintext_http_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        assert resolve_openai_workload_identity_config(api_key=None, api_base="http://api.openai.com/v1") is None
+
+    def test_foreign_env_base_url_disables(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("OPENAI_BASE_URL", "https://my-vllm.internal/v1")
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None
+
+    def test_openai_env_base_url_allows(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env
+
+    def test_foreign_litellm_api_base_disables(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setattr(litellm, "api_base", "https://my-vllm.internal/v1")
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None
+
+    @pytest.mark.parametrize(
+        "missing_var",
+        ["OPENAI_IDENTITY_PROVIDER_ID", "OPENAI_SERVICE_ACCOUNT_ID", "OPENAI_IDENTITY_TOKEN_FILE"],
+    )
+    def test_partial_env_disables(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, missing_var: str
+    ) -> None:
+        monkeypatch.delenv(missing_var)
+        assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None
+
+
+class TestTokenExchange:
+    @respx.mock
+    def test_exchanges_subject_token_for_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        route: Final = mock_token_exchange()
+        assert get_workload_identity_bearer_token(wif_env) == "exchanged-bearer-token"
+        request_body: Final = json.loads(route.calls.last.request.content)
+        assert request_body["grant_type"] == "urn:ietf:params:oauth:grant-type:token-exchange"
+        assert request_body["subject_token"] == "subject-token-from-file"
+        assert request_body["subject_token_type"] == "urn:ietf:params:oauth:token-type:jwt"
+        assert request_body["identity_provider_id"] == "idp_test123"
+        assert request_body["service_account_id"] == "user-test456"
+
+    @respx.mock
+    def test_token_cached_across_mints(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        route: Final = mock_token_exchange()
+        first: Final = get_workload_identity_bearer_token(wif_env)
+        second: Final = get_workload_identity_bearer_token(wif_env)
+        assert first == second == "exchanged-bearer-token"
+        assert route.call_count == 1
+
+    def test_old_sdk_raises_upgrade_error(
+        self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        import openai as openai_module
+
+        monkeypatch.delattr(openai_module, "auth", raising=False)
+        monkeypatch.setitem(sys.modules, "openai.auth", None)
+        with pytest.raises(OpenAIError, match=r"openai>=2\.32\.0"):
+            wif_env.to_sdk_workload_identity()
+
+
+class TestClientConstruction:
+    def test_sync_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None)
+        assert isinstance(client, OpenAI)
+        assert client.api_key == "workload-identity-auth"
+        assert client._workload_identity_auth is not None
+
+    def test_async_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        client: Final = OpenAIChatCompletion()._get_openai_client(is_async=True, api_key=None, api_base=None)
+        assert isinstance(client, AsyncOpenAI)
+        assert client.api_key == "workload-identity-auth"
+        assert client._workload_identity_auth is not None
+
+    def test_static_key_client_unaffected(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key="sk-static", api_base=None)
+        assert isinstance(client, OpenAI)
+        assert client.api_key == "sk-static"
+        assert client._workload_identity_auth is None
+
+    def test_cache_key_separates_wif_identities(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        other_config: Final = OpenAIWorkloadIdentityConfig(
+            identity_provider_id="idp_other",
+            service_account_id="user-other",
+            token_file=wif_env.token_file,
+        )
+        keys: Final = tuple(
+            BaseOpenAILLM.get_openai_client_cache_key(
+                client_initialization_params={"api_key": None, "is_async": False, "workload_identity_config": config},
+                client_type="openai",
+            )
+            for config in (wif_env, other_config, None)
+        )
+        assert len(set(keys)) == 3
+
+    @respx.mock
+    def test_request_carries_exchanged_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        mock_token_exchange()
+        completion_route: Final = respx.post("https://api.openai.com/v1/chat/completions").mock(
+            return_value=httpx.Response(
+                200,
+                json={
+                    "id": "chatcmpl-wif",
+                    "object": "chat.completion",
+                    "created": 1,
+                    "model": "gpt-4o-mini",
+                    "choices": [
+                        {
+                            "index": 0,
+                            "message": {"role": "assistant", "content": "ok"},
+                            "finish_reason": "stop",
+                        }
+                    ],
+                    "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
+                },
+            )
+        )
+        client = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None)
+        assert isinstance(client, OpenAI)
+        client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": "hi"}])
+        auth_header: Final = completion_route.calls.last.request.headers["Authorization"]
+        assert auth_header == "Bearer exchanged-bearer-token"
+
+
+class TestResponsesValidateEnvironment:
+    @respx.mock
+    def test_mints_bearer_when_wif_configured(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        mock_token_exchange()
+        headers: Final = OpenAIResponsesAPIConfig().validate_environment(
+            headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams()
+        )
+        assert headers["Authorization"] == "Bearer exchanged-bearer-token"
+
+    def test_static_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        headers: Final = OpenAIResponsesAPIConfig().validate_environment(
+            headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams(api_key="sk-responses")
+        )
+        assert headers["Authorization"] == "Bearer sk-responses"
+
+    def test_foreign_api_base_skips_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        headers: Final = OpenAIResponsesAPIConfig().validate_environment(
+            headers={},
+            model="gpt-4o-mini",
+            litellm_params=GenericLiteLLMParams(api_base="https://my-vllm.internal/v1"),
+        )
+        assert headers["Authorization"] == "Bearer None"
+
+    def test_litellm_proxy_subclass_never_mints_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None:
+        headers: Final = LiteLLMProxyResponsesAPIConfig().validate_environment(
+            headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams()
+        )
+        assert headers["Authorization"] == "Bearer None"
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
index 29d22e844a5..a4d67606698 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
@@ -982,6 +982,116 @@ class TestVertexBase:
 
         assert result_url == f"{gateway_api_base}:embedContent"
 
+    def test_check_custom_proxy_vertex_api_base_with_version_path_grafts_default_path(self):
+        vertex_base = VertexBase()
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base="https://aiplatform.googleapis.com/v1beta1",
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="generateContent",
+            stream=None,
+            auth_header="Bearer token123",
+            url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent",
+            model="gemini-3.5-flash-lite",
+        )
+
+        assert (
+            result_url
+            == "https://aiplatform.googleapis.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent"
+        )
+
+    def test_check_custom_proxy_vertex_api_base_with_version_path_trailing_slash_grafts_default_path(self):
+        vertex_base = VertexBase()
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base="https://internal-gateway.example.com/v1/",
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="generateContent",
+            stream=None,
+            auth_header="Bearer token123",
+            url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent",
+            model="gemini-3.5-flash-lite",
+        )
+
+        assert (
+            result_url
+            == "https://internal-gateway.example.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent"
+        )
+
+    def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_grafts_before_query(self):
+        vertex_base = VertexBase()
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base="https://internal-gateway.example.com/v1beta1?key=abc",
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="generateContent",
+            stream=None,
+            auth_header="Bearer token123",
+            url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent",
+            model="gemini-3.5-flash-lite",
+        )
+
+        assert (
+            result_url
+            == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent?key=abc"
+        )
+
+    def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_streaming_appends_alt_sse(self):
+        vertex_base = VertexBase()
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base="https://internal-gateway.example.com/v1beta1?key=abc",
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="streamGenerateContent",
+            stream=True,
+            auth_header="Bearer token123",
+            url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent",
+            model="gemini-3.5-flash-lite",
+        )
+
+        assert (
+            result_url
+            == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent?key=abc&alt=sse"
+        )
+
+    def test_check_custom_proxy_vertex_api_base_with_non_version_path_keeps_endpoint_append(self):
+        vertex_base = VertexBase()
+        gateway_api_base = "https://gateway.example.com/vertex-proxy"
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base=gateway_api_base,
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="generateContent",
+            stream=None,
+            auth_header="Bearer token123",
+            url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent",
+            model="gemini-3.5-flash-lite",
+        )
+
+        assert result_url == f"{gateway_api_base}:generateContent"
+
+    def test_check_custom_proxy_vertex_api_base_without_projects_in_default_url_keeps_endpoint_append(self):
+        vertex_base = VertexBase()
+        gemma_api_base = "https://example.com/custom/gemma-deployment"
+
+        _, result_url = vertex_base._check_custom_proxy(
+            api_base=gemma_api_base,
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="predict",
+            stream=False,
+            auth_header=None,
+            url=gemma_api_base,
+            model="gemma-3-27b-it",
+        )
+
+        assert result_url == f"{gemma_api_base}:predict"
+
     def test_check_custom_proxy_vertex_bare_host_streaming_keeps_single_alt_sse(self):
         vertex_base = VertexBase()
 
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py
index b140082a3bf..f5d51a601d7 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py
@@ -432,7 +432,7 @@ class TestHiddenlayerGuardrail:
 
     @pytest.mark.asyncio
     async def test_apply_guardrail_request_with_image(self, monkeypatch: pytest.MonkeyPatch):
-        """Test apply_guardrail sends multimodal content (image) to HiddenLayer v1."""
+        """Test apply_guardrail strips images from multimodal content before sending to HiddenLayer v1."""
         monkeypatch.setenv("HIDDENLAYER_API_BASE", "https://my.hiddenlayer")
 
         guardrail = HiddenlayerGuardrail(
@@ -485,12 +485,13 @@ class TestHiddenlayerGuardrail:
                 logging_obj=logging_obj,
             )
 
-        # v1 API requires string content — multimodal list is stringified
+        # v1 API requires string content — image_url items are stripped and the
+        # remaining (text-only) content is stringified before being sent.
         mock_post.assert_called_once()
         call_kwargs = mock_post.call_args.kwargs
         sent_content = call_kwargs["json"]["input"]["messages"][0]["content"]
         assert isinstance(sent_content, str)
-        assert sent_content == str(multimodal_content)
+        assert sent_content == str([{"type": "text", "text": "how much is on this receipt?"}])
 
         # Result should be returned without error
         assert result is not None
diff --git a/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py b/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py
index 26beaa78a46..ab4e15ff423 100644
--- a/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py
+++ b/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py
@@ -1,16 +1,16 @@
-from fastapi.exceptions import HTTPException
-from unittest.mock import patch, AsyncMock
-from httpx import Response, Request
+import asyncio
 import base64
+from unittest.mock import AsyncMock, patch
 
 import pytest
-
-from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import (
-    PromptSecurityGuardrailMissingSecrets,
-    PromptSecurityGuardrail,
-)
+from fastapi.exceptions import HTTPException
+from httpx import ReadTimeout, Request, Response
 
 import litellm
+from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import (
+    PromptSecurityGuardrail,
+    PromptSecurityGuardrailMissingSecrets,
+)
 from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2
 
 
@@ -30,6 +30,7 @@ def test_prompt_security_guard_config(monkeypatch: pytest.MonkeyPatch):
                     "guardrail": "prompt_security",
                     "mode": "during_call",
                     "default_on": True,
+                    "file_sanitization_fail_open": False,
                 },
             }
         ],
@@ -41,6 +42,10 @@ def test_prompt_security_guard_config(monkeypatch: pytest.MonkeyPatch):
     assert registered[0].guardrail_name == "prompt_security"
     assert registered[0].default_on is True
     assert registered[0].event_hook == "during_call"
+    assert registered[0].file_sanitization_fail_open is False
+    config_model = registered[0].get_config_model()
+    assert config_model is not None
+    assert config_model().file_sanitization_fail_open is True
 
 
 def test_prompt_security_guard_config_no_api_key(monkeypatch: pytest.MonkeyPatch):
@@ -374,6 +379,86 @@ async def test_file_sanitization(monkeypatch: pytest.MonkeyPatch):
     assert result is not None
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "timeout",
+    (
+        litellm.Timeout(
+            message="Prompt Security upload timed out",
+            model="default-model-name",
+            llm_provider="litellm-httpx-handler",
+        ),
+        ReadTimeout(
+            "Prompt Security poll timed out",
+            request=Request(method="GET", url="https://test.prompt.security/api/sanitizeFile"),
+        ),
+    ),
+    ids=("litellm", "httpx"),
+)
+@pytest.mark.parametrize("fail_open", (True, False), ids=("fail-open", "fail-closed"))
+async def test_file_sanitization_request_timeout_policy(
+    monkeypatch: pytest.MonkeyPatch, timeout: Exception, fail_open: bool
+):
+    monkeypatch.setenv("PROMPT_SECURITY_API_KEY", "test-key")
+    monkeypatch.setenv("PROMPT_SECURITY_API_BASE", "https://test.prompt.security")
+
+    guardrail = PromptSecurityGuardrail(
+        guardrail_name="test-guard",
+        event_hook="pre_call",
+        default_on=True,
+        file_sanitization_fail_open=fail_open,
+    )
+
+    with patch.object(guardrail.async_handler, "post", AsyncMock(side_effect=timeout)):
+        if not fail_open:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.sanitize_file_content(b"file-content", "document.pdf")
+            assert exc_info.value.status_code == 408
+            assert exc_info.value.detail == "File sanitization timeout"
+            return
+
+        result = await guardrail.sanitize_file_content(b"file-content", "document.pdf")
+
+    assert result == {
+        "action": "allow",
+        "content": None,
+        "metadata": {},
+        "violations": (),
+    }
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("fail_open", (True, False), ids=("fail-open", "fail-closed"))
+async def test_file_sanitization_overall_timeout_policy(monkeypatch: pytest.MonkeyPatch, fail_open: bool):
+    monkeypatch.setenv("PROMPT_SECURITY_API_KEY", "test-key")
+    monkeypatch.setenv("PROMPT_SECURITY_API_BASE", "https://test.prompt.security")
+
+    guardrail = PromptSecurityGuardrail(
+        guardrail_name="test-guard",
+        event_hook="pre_call",
+        default_on=True,
+        file_sanitization_timeout=0.01,
+        file_sanitization_fail_open=fail_open,
+    )
+
+    async def hanging_post(*_args: object, **_kwargs: object) -> None:
+        await asyncio.sleep(60)
+        raise AssertionError("sanitization request should have been cancelled")
+
+    with patch.object(guardrail.async_handler, "post", side_effect=hanging_post):
+        if not fail_open:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.sanitize_file_content(b"file-content", "document.pdf")
+            assert exc_info.value.status_code == 408
+            assert exc_info.value.detail == "File sanitization timeout"
+            return
+
+        result = await guardrail.sanitize_file_content(b"file-content", "document.pdf")
+
+    assert result["action"] == "allow"
+    assert result["content"] is None
+
+
 @pytest.mark.asyncio
 async def test_file_sanitization_block(monkeypatch: pytest.MonkeyPatch):
     """Test that file sanitization blocks malicious files"""
@@ -544,7 +629,7 @@ async def test_role_filtering(monkeypatch: pytest.MonkeyPatch):
         return mock_response
 
     with patch.object(guardrail.async_handler, "post", side_effect=mock_post):
-        result = await guardrail.apply_guardrail(
+        await guardrail.apply_guardrail(
             inputs=inputs,
             request_data=request_data,
             input_type="request",
diff --git a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py
index 5c163c44cb3..1225cb80224 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py
@@ -83,6 +83,50 @@ def test_update_customer_success(mock_prisma_client, mock_user_api_key_auth):
     assert response.json()["alias"] == "Updated Test User"
 
 
+def test_update_customer_unblock(mock_prisma_client, mock_user_api_key_auth):
+    mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True)
+    updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=False)
+
+    mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user)
+    mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user)
+
+    response = client.post(
+        "/customer/update",
+        json={"user_id": "test-user-1", "blocked": False},
+        headers={"Authorization": "Bearer test-key"},
+    )
+
+    assert response.status_code == 200
+    assert response.json()["blocked"] is False
+    update_mock = mock_prisma_client.db.litellm_endusertable.update
+    update_mock.assert_called_once()
+    assert update_mock.call_args.kwargs["data"]["blocked"] is False
+
+
+def test_update_customer_keeps_blocked_when_omitted(mock_prisma_client, mock_user_api_key_auth):
+    """
+    Regression test: updating a blocked customer without supplying `blocked`
+    must NOT reset it to unblocked. `blocked=False` is the model default and
+    should only be applied when explicitly provided by the caller.
+    """
+    mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True)
+    updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True)
+
+    mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user)
+    mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user)
+
+    response = client.post(
+        "/customer/update",
+        json={"user_id": "test-user-1", "alias": "Updated Test User"},
+        headers={"Authorization": "Bearer test-key"},
+    )
+
+    assert response.status_code == 200
+    update_mock = mock_prisma_client.db.litellm_endusertable.update
+    update_mock.assert_called_once()
+    assert "blocked" not in update_mock.call_args.kwargs["data"]
+
+
 def test_update_customer_not_found(mock_prisma_client, mock_user_api_key_auth):
     """
     Test that update_end_user raises a 404 ProxyException when user_id does not exist.
diff --git a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py
index 791d64c6428..d7010de6405 100644
--- a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py
+++ b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py
@@ -3,10 +3,12 @@ Test for response_api_endpoints/endpoints.py
 """
 
 import unittest
+from typing import Any
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from fastapi.testclient import TestClient
+from httpx import Response
 
 import litellm
 from litellm.proxy.proxy_server import app
@@ -82,11 +84,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase):
                 ResponseOutputMessage(
                     type="message",
                     role="assistant",
-                    content=[
-                        ResponseOutputText(
-                            type="output_text", text="Hello from Cursor!"
-                        )
-                    ],
+                    content=[ResponseOutputText(type="output_text", text="Hello from Cursor!")],
                 )
             ],
         )
@@ -121,9 +119,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase):
     @pytest.mark.asyncio
     @patch("litellm.proxy.proxy_server.llm_router")
     @patch("litellm.proxy.proxy_server.user_api_key_auth")
-    async def test_responses_api_key_spend_header_includes_response_cost(
-        self, mock_auth, mock_router
-    ):
+    async def test_responses_api_key_spend_header_includes_response_cost(self, mock_auth, mock_router):
         """
         Test that x-litellm-key-spend header includes the current request's response_cost
         for /v1/responses endpoint.
@@ -159,9 +155,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase):
                 ResponseOutputMessage(
                     type="message",
                     role="assistant",
-                    content=[
-                        ResponseOutputText(type="output_text", text="Test response")
-                    ],
+                    content=[ResponseOutputText(type="output_text", text="Test response")],
                 )
             ],
         )
@@ -356,6 +350,7 @@ class TestWSModelExtraction:
         from litellm.proxy.response_api_endpoints.endpoints import (
             _extract_model_from_first_ws_event,
         )
+
         event = {"type": "response.create", "model": "gpt-4o", "input": "hello"}
         assert _extract_model_from_first_ws_event(event) == "gpt-4o"
 
@@ -363,6 +358,7 @@ class TestWSModelExtraction:
         from litellm.proxy.response_api_endpoints.endpoints import (
             _extract_model_from_first_ws_event,
         )
+
         event = {"type": "response.create", "response": {"model": "gpt-4o", "input": "hello"}}
         assert _extract_model_from_first_ws_event(event) == "gpt-4o"
 
@@ -370,6 +366,7 @@ class TestWSModelExtraction:
         from litellm.proxy.response_api_endpoints.endpoints import (
             _extract_model_from_first_ws_event,
         )
+
         event = {
             "type": "response.create",
             "model": "flat-model",
@@ -381,6 +378,7 @@ class TestWSModelExtraction:
         from litellm.proxy.response_api_endpoints.endpoints import (
             _extract_model_from_first_ws_event,
         )
+
         event = {"type": "response.create", "input": "hello"}
         assert _extract_model_from_first_ws_event(event) is None
 
@@ -400,9 +398,7 @@ class TestResponsesWSFirstFrameValidation:
         )
 
         ws = MagicMock()
-        ws.receive_text = AsyncMock(
-            return_value=json.dumps({"type": "session.update", "model": "gpt-4o"})
-        )
+        ws.receive_text = AsyncMock(return_value=json.dumps({"type": "session.update", "model": "gpt-4o"}))
         ws.send_text = AsyncMock()
         ws.close = AsyncMock()
 
@@ -412,10 +408,7 @@ class TestResponsesWSFirstFrameValidation:
         ws.send_text.assert_awaited_once()
         ws.close.assert_awaited_once_with(code=1008, reason="Invalid first message")
         error_payload = json.loads(ws.send_text.await_args.args[0])
-        assert (
-            error_payload["error"]["message"]
-            == "First message must be a response.create JSON object."
-        )
+        assert error_payload["error"]["message"] == "First message must be a response.create JSON object."
 
     @pytest.mark.asyncio
     async def test_rejects_non_object_json_first_frame(self):
@@ -484,16 +477,12 @@ class TestResponsesWSFirstFrameModelAuth:
         ws.url = "ws://testserver/v1/responses"
         ws.accept = AsyncMock()
         ws.receive_text = AsyncMock(
-            return_value=json.dumps(
-                {"type": "response.create", "model": "gpt-4o-mini", "input": []}
-            )
+            return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []})
         )
         ws.close = AsyncMock()
 
         processor = MagicMock()
-        processor.common_processing_pre_call_logic = AsyncMock(
-            return_value=({"model": "gpt-4o-mini"}, MagicMock())
-        )
+        processor.common_processing_pre_call_logic = AsyncMock(return_value=({"model": "gpt-4o-mini"}, MagicMock()))
 
         async def fake_llm_call():
             return None
@@ -529,9 +518,7 @@ class TestResponsesWSFirstFrameModelAuth:
             _enforce_responses_ws_first_frame_model_auth,
         )
 
-        request = Request(
-            {"type": "http", "method": "POST", "path": "/v1/responses", "headers": []}
-        )
+        request = Request({"type": "http", "method": "POST", "path": "/v1/responses", "headers": []})
         user_api_key_dict = MagicMock()
         llm_router = MagicMock()
 
@@ -593,9 +580,7 @@ class TestReadWSModelFromFirstFrameErrors:
 
         assert result is None
         ws.send_text.assert_not_awaited()
-        ws.close.assert_awaited_once_with(
-            code=1008, reason="Timed out waiting for first message"
-        )
+        ws.close.assert_awaited_once_with(code=1008, reason="Timed out waiting for first message")
 
     @pytest.mark.asyncio
     async def test_invalid_json_sends_error_and_closes(self):
@@ -613,9 +598,7 @@ class TestReadWSModelFromFirstFrameErrors:
         assert result is None
         payload = json.loads(ws.send_text.await_args.args[0])
         assert payload["error"]["message"] == "First message is not valid JSON."
-        ws.close.assert_awaited_once_with(
-            code=1008, reason="Invalid JSON in first message"
-        )
+        ws.close.assert_awaited_once_with(code=1008, reason="Invalid JSON in first message")
 
     @pytest.mark.asyncio
     async def test_missing_model_sends_error_and_closes(self):
@@ -624,9 +607,7 @@ class TestReadWSModelFromFirstFrameErrors:
         )
 
         ws = MagicMock()
-        ws.receive_text = AsyncMock(
-            return_value=json.dumps({"type": "response.create", "input": []})
-        )
+        ws.receive_text = AsyncMock(return_value=json.dumps({"type": "response.create", "input": []}))
         ws.send_text = AsyncMock()
         ws.close = AsyncMock()
 
@@ -679,10 +660,7 @@ class TestManagedResponsesSameProvider:
         assert self._handler("gpt-4o")._same_provider("gpt-4o-mini") is True
 
     def test_different_provider_is_not_same(self):
-        assert (
-            self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash")
-            is False
-        )
+        assert self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") is False
 
     def test_inject_credentials_keeps_provider_for_same_provider_model(self):
         handler = self._handler("gpt-4o", custom_llm_provider="openai")
@@ -697,18 +675,14 @@ class TestManagedResponsesSameProvider:
         assert "custom_llm_provider" not in call_kwargs
 
     def test_unresolvable_connection_model_falls_back_to_custom_provider(self):
-        handler = self._handler(
-            "my-custom-deployment", custom_llm_provider="openai"
-        )
+        handler = self._handler("my-custom-deployment", custom_llm_provider="openai")
         assert handler._same_provider("gpt-4o-mini") is True
         call_kwargs: dict = {}
         handler._inject_credentials(call_kwargs, model="gpt-4o-mini")
         assert call_kwargs["custom_llm_provider"] == "openai"
 
     def test_unresolvable_connection_model_still_drops_cross_provider(self):
-        handler = self._handler(
-            "my-custom-deployment", custom_llm_provider="openai"
-        )
+        handler = self._handler("my-custom-deployment", custom_llm_provider="openai")
         call_kwargs: dict = {}
         handler._inject_credentials(call_kwargs, model="vertex_ai/gemini-2.0-flash")
         assert "custom_llm_provider" not in call_kwargs
@@ -840,9 +814,7 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s
                     type="message",
                     role="assistant",
                     status="completed",
-                    content=[
-                        ResponseOutputText(type="output_text", text="agent reply", annotations=[])
-                    ],
+                    content=[ResponseOutputText(type="output_text", text="agent reply", annotations=[])],
                 )
             ],
         )
@@ -851,9 +823,12 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s
 
     app.dependency_overrides[user_api_key_auth] = _auth_override
     try:
-        with patch.object(ps, "llm_router", mock_router), patch(
-            "litellm.proxy.response_api_endpoints.endpoints._read_request_body",
-            side_effect=capturing_read_request_body,
+        with (
+            patch.object(ps, "llm_router", mock_router),
+            patch(
+                "litellm.proxy.response_api_endpoints.endpoints._read_request_body",
+                side_effect=capturing_read_request_body,
+            ),
         ):
             client = TestClient(app)
             response = client.post(
@@ -1488,8 +1463,8 @@ def _router_serving_only(base_model: str) -> MagicMock:
     mock_router.router_general_settings.pass_through_all_models = False
     mock_router.default_deployment = None
     mock_router.pattern_router.patterns = {base_model: ["anthropic/*"]}
-    mock_router.pattern_router.get_pattern.side_effect = (
-        lambda model: [{"model_name": "anthropic/*"}] if model == base_model else None
+    mock_router.pattern_router.get_pattern.side_effect = lambda model: (
+        [{"model_name": "anthropic/*"}] if model == base_model else None
     )
     return mock_router
 
@@ -1739,9 +1714,7 @@ class TestCursorGateRecognizesRoutingGroups:
         from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant
 
         router = Router(
-            model_list=[
-                {"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}}
-            ],
+            model_list=[{"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}}],
             routing_groups=[
                 {"group_name": "grouped-thinking-high", "models": ["member-fast"], "routing_strategy": "simple-shuffle"}
             ],
@@ -1836,3 +1809,153 @@ class TestGuardrailBlockedResponsesUsage:
         assert usage["input_tokens"] == 0
         assert usage["output_tokens"] == 0
         assert usage["total_tokens"] == 0
+
+
+class TestResponsesInputTokens:
+    """Regression tests for POST /v1/responses/input_tokens.
+
+    The docs promise OpenAI-format token counting on the proxy, but the route was
+    never registered, so the POST fell through to the GET/DELETE-only
+    /v1/responses/{response_id} route and returned 405."""
+
+    def _post_input_tokens(
+        self,
+        body: dict[str, Any],
+        path: str = "/v1/responses/input_tokens",
+        counter: AsyncMock | None = None,
+    ) -> tuple[Response, AsyncMock]:
+        from litellm.proxy._types import UserAPIKeyAuth
+        from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+        from litellm.proxy.response_api_endpoints.endpoints import _proxy_token_counter
+        from litellm.types.utils import TokenCountResponse
+
+        token_counter_mock = (
+            counter
+            if counter is not None
+            else AsyncMock(
+                return_value=TokenCountResponse(
+                    total_tokens=13,
+                    request_model=body.get("model", ""),
+                    model_used=body.get("model", ""),
+                    tokenizer_type="openai_api",
+                )
+            )
+        )
+        app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-test", request_route=path)
+        app.dependency_overrides[_proxy_token_counter] = lambda: token_counter_mock
+        try:
+            client = TestClient(app)
+            response = client.post(path, json=body, headers={"Authorization": "Bearer sk-1234"})
+            return response, token_counter_mock
+        finally:
+            app.dependency_overrides.pop(user_api_key_auth, None)
+            app.dependency_overrides.pop(_proxy_token_counter, None)
+
+    def test_string_input_returns_openai_input_tokens_shape(self):
+        response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "Hello, how are you?"})
+
+        assert response.status_code == 200, response.text
+        assert response.json() == {"object": "response.input_tokens", "input_tokens": 13}
+        counter.assert_awaited_once()
+        assert counter.call_args.kwargs["call_endpoint"] is True
+        token_request = counter.call_args.kwargs["request"]
+        assert token_request.model == "gpt-4o"
+        assert token_request.messages == [{"role": "user", "content": "Hello, how are you?"}]
+
+    def test_every_route_alias_is_registered(self):
+        for path in ("/v1/responses/input_tokens", "/responses/input_tokens", "/openai/v1/responses/input_tokens"):
+            response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, path=path)
+            assert response.status_code == 200, f"{path}: {response.status_code} {response.text}"
+
+    def test_input_items_instructions_and_tools_are_forwarded(self):
+        tools = [
+            {
+                "type": "function",
+                "name": "get_weather",
+                "description": "Get weather for a city",
+                "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
+            }
+        ]
+        response, counter = self._post_input_tokens(
+            {
+                "model": "gpt-4o",
+                "input": [{"role": "user", "content": "What is the weather in Paris?"}],
+                "instructions": "You are terse.",
+                "tools": tools,
+            }
+        )
+
+        assert response.status_code == 200, response.text
+        token_request = counter.call_args.kwargs["request"]
+        assert token_request.messages == [
+            {"role": "system", "content": "You are terse."},
+            {"role": "user", "content": "What is the weather in Paris?"},
+        ]
+        assert token_request.tools == tools
+
+    def test_missing_model_returns_openai_400(self):
+        response, counter = self._post_input_tokens({"input": "Hello"})
+
+        assert response.status_code == 400, response.text
+        assert response.json() == {
+            "error": {
+                "message": "Missing required parameter: 'model'.",
+                "type": "invalid_request_error",
+                "param": "model",
+                "code": "missing_required_parameter",
+            }
+        }
+        counter.assert_not_awaited()
+
+    def test_missing_input_returns_openai_400(self):
+        response, counter = self._post_input_tokens({"model": "gpt-4o"})
+
+        assert response.status_code == 400, response.text
+        assert response.json() == {
+            "error": {
+                "message": "Missing required parameter: 'input'.",
+                "type": "invalid_request_error",
+                "param": "input",
+                "code": "missing_required_parameter",
+            }
+        }
+        counter.assert_not_awaited()
+
+    @pytest.mark.parametrize("empty_input", ["", []])
+    def test_empty_input_returns_openai_400(self, empty_input):
+        response, counter = self._post_input_tokens({"model": "gpt-4o", "input": empty_input})
+
+        assert response.status_code == 400, response.text
+        assert response.json() == {
+            "error": {
+                "message": """One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""",
+                "type": "invalid_request_error",
+                "param": None,
+                "code": "missing_required_parameter",
+            }
+        }
+        counter.assert_not_awaited()
+
+    def test_invalid_tools_returns_openai_400(self):
+        response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "hi", "tools": "not-a-list"})
+
+        assert response.status_code == 400, response.text
+        error = response.json()["error"]
+        assert error["type"] == "invalid_request_error"
+        counter.assert_not_awaited()
+
+    def test_provider_error_maps_status_code(self):
+        from litellm.proxy._types import ProxyException
+
+        failing_counter = AsyncMock(
+            side_effect=ProxyException(
+                message="rate limited",
+                type="token_counting_error",
+                param="model",
+                code="429",
+            )
+        )
+        response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, counter=failing_counter)
+
+        assert response.status_code == 429, response.text
+        assert response.json()["error"]["message"] == "rate limited"
diff --git a/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py
new file mode 100644
index 00000000000..f65f68812a2
--- /dev/null
+++ b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py
@@ -0,0 +1,48 @@
+from typing import Final
+
+import pytest
+
+from litellm.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache
+from litellm.proxy.spend_tracking.budget_reservation import reserve_budget_for_request
+from litellm.proxy.utils import ProxyLogging
+
+TOKEN_COUNTING_ROUTES: Final = (
+    "/responses/input_tokens",
+    "/v1/responses/input_tokens",
+    "/openai/v1/responses/input_tokens",
+    "/utils/token_counter",
+)
+
+
+def _budgeted_token() -> UserAPIKeyAuth:
+    return UserAPIKeyAuth(api_key="sk-test", token="hashed-token", max_budget=100.0, spend=0.0)
+
+
+async def _reserve(route: str) -> dict | None:
+    return await reserve_budget_for_request(
+        request_body={"model": "gpt-4o", "input": "hello"},
+        route=route,
+        llm_router=None,
+        valid_token=_budgeted_token(),
+        team_object=None,
+        user_object=None,
+        prisma_client=None,
+        user_api_key_cache=UserApiKeyCache(),
+        proxy_logging_obj=ProxyLogging(user_api_key_cache=DualCache()),
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("route", TOKEN_COUNTING_ROUTES)
+async def test_token_counting_routes_are_exempt_from_budget_reservation(route):
+    assert await _reserve(route) is None
+
+
+@pytest.mark.asyncio
+async def test_non_exempt_llm_route_still_reserves_budget():
+    reservation: Final = await _reserve("/v1/responses")
+
+    assert reservation is not None
+    assert reservation["reserved_cost"] > 0
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
index 10c3e5fecf8..a0dcbf802ef 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
@@ -2865,7 +2865,7 @@ class TestSpendLogsPayload:
                     "model": "gpt-4o",
                     "user": "",
                     "team_id": "",
-                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 20, "prompt_tokens": 10, "total_tokens": 30, "completion_tokens_details": null, "prompt_tokens_details": null}, "model_map_information": {"model_map_key": "gpt-4o", "model_map_value": {"key": "gpt-4o", "max_tokens": 16384, "max_input_tokens": 128000, "max_output_tokens": 16384, "input_cost_per_token": 2.5e-06, "cache_creation_input_token_cost": null, "cache_read_input_token_cost": 1.25e-06, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": 1.25e-06, "output_cost_per_token_batches": 5e-06, "output_cost_per_token": 1e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_reasoning_token": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "openai", "mode": "chat", "supports_system_messages": true, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": false, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": false, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": true, "supports_reasoning": false, "search_context_cost_per_query": {"search_context_size_low": 0.03, "search_context_size_medium": 0.035, "search_context_size_high": 0.05}, "tpm": null, "rpm": null, "supported_openai_params": ["frequency_penalty", "logit_bias", "logprobs", "top_logprobs", "max_tokens", "max_completion_tokens", "modalities", "prediction", "n", "presence_penalty", "seed", "stop", "stream", "stream_options", "temperature", "top_p", "tools", "tool_choice", "function_call", "functions", "max_retries", "extra_headers", "parallel_tool_calls", "audio", "response_format", "user"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": null}}',
+                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 20, "prompt_tokens": 10, "total_tokens": 30, "completion_tokens_details": null, "prompt_tokens_details": null}, "model_map_information": {"model_map_key": "gpt-4o", "model_map_value": {"key": "gpt-4o", "max_tokens": 16384, "max_input_tokens": 128000, "max_output_tokens": 16384, "input_cost_per_token": 2.5e-06, "cache_creation_input_token_cost": null, "cache_read_input_token_cost": 1.25e-06, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": 1.25e-06, "output_cost_per_token_batches": 5e-06, "output_cost_per_token": 1e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_reasoning_token": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "openai", "mode": "chat", "supports_system_messages": true, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": false, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": false, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": true, "supports_reasoning": false, "search_context_cost_per_query": {"search_context_size_low": 0.03, "search_context_size_medium": 0.035, "search_context_size_high": 0.05}, "tpm": null, "rpm": null, "supported_openai_params": ["frequency_penalty", "logit_bias", "logprobs", "top_logprobs", "max_tokens", "max_completion_tokens", "modalities", "prediction", "n", "presence_penalty", "seed", "stop", "stream", "stream_options", "temperature", "top_p", "tools", "tool_choice", "function_call", "functions", "max_retries", "extra_headers", "parallel_tool_calls", "audio", "response_format", "user"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": null}}',
                     "cache_key": "Cache OFF",
                     "spend": 0.00022500000000000002,
                     "total_tokens": 30,
@@ -2961,7 +2961,7 @@ class TestSpendLogsPayload:
                     "model": "claude-4-sonnet-20250514",
                     "user": "",
                     "team_id": "",
-                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
+                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
                     "cache_key": "Cache OFF",
                     "spend": 0.01383,
                     "total_tokens": 2598,
@@ -3055,7 +3055,7 @@ class TestSpendLogsPayload:
                     "model": "claude-4-sonnet-20250514",
                     "user": "",
                     "team_id": "",
-                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": 0, "original_model_group": "my-anthropic-model-group", "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
+                    "metadata": '{"applied_guardrails": [], "attempted_fallbacks": 0, "original_model_group": "my-anthropic-model-group", "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
                     "cache_key": "Cache OFF",
                     "spend": 0.01383,
                     "total_tokens": 2598,
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
index 5022dab32be..9e5917637a8 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
@@ -2,6 +2,7 @@ import asyncio
 import datetime
 import json
 from datetime import timezone
+from collections.abc import Mapping
 from typing import Any, Final, cast
 from unittest.mock import AsyncMock, MagicMock, patch
 
@@ -3956,3 +3957,71 @@ def test_passthrough_caching_carries_no_injection_marker():
     )
     metadata = json.loads(payload["metadata"])
     assert metadata["litellm_gateway_injected_cache"] is None
+
+
+def _routed_call_kwargs(model_info: Mapping[str, object]) -> dict[str, object]:
+    return {
+        "model": "claude-haiku-4-5",
+        "custom_llm_provider": "azure_ai",
+        "litellm_call_id": "router-corr-123",
+        "litellm_params": {
+            "metadata": {
+                "user_api_key": "test-key",
+                "model_group": "internal-router/gpt-5.4",
+                "deployment": "azure_ai/claude-haiku-4-5",
+                "model_info": model_info,
+            }
+        },
+    }
+
+
+def test_router_metadata_stamped_for_internal_router_model_deployment():
+    """A deployment flagged model_info.internal_router_model gets a router_metadata
+    block correlating the requested model group with the selected deployment."""
+    payload = get_logging_payload(
+        kwargs=_routed_call_kwargs({"id": "mi-1", "internal_router_model": True}),
+        response_obj=litellm.ModelResponse(id="chatcmpl-router-meta", choices=[], usage=litellm.Usage()),
+        start_time=datetime.datetime.now(timezone.utc),
+        end_time=datetime.datetime.now(timezone.utc),
+    )
+    metadata = json.loads(payload["metadata"])
+    assert metadata["router_metadata"] == {
+        "requested_model": "internal-router/gpt-5.4",
+        "selected_model": "azure_ai/claude-haiku-4-5",
+        "selected_provider": "azure_ai",
+        "router_correlation_id": "router-corr-123",
+    }
+
+
+def test_router_metadata_absent_without_internal_router_model_flag():
+    payload = get_logging_payload(
+        kwargs=_routed_call_kwargs({"id": "mi-1"}),
+        response_obj=litellm.ModelResponse(id="chatcmpl-unflagged", choices=[], usage=litellm.Usage()),
+        start_time=datetime.datetime.now(timezone.utc),
+        end_time=datetime.datetime.now(timezone.utc),
+    )
+    metadata = json.loads(payload["metadata"])
+    assert metadata["router_metadata"] is None
+
+
+@pytest.mark.parametrize("bucket", ["metadata", "litellm_metadata"])
+def test_caller_forged_router_metadata_is_discarded(bucket):
+    """The raw request bucket is client-writable and _get_spend_logs_metadata projects
+    every SpendLogsMetadata key from it, so the server-derived value must overwrite
+    unconditionally or a caller could plant router provenance the router never produced."""
+    payload = get_logging_payload(
+        kwargs={
+            "model": "gpt-4o-mini",
+            "litellm_params": {
+                bucket: {
+                    "user_api_key": "test-key",
+                    "router_metadata": {"requested_model": "forged", "router_correlation_id": "forged-id"},
+                }
+            },
+        },
+        response_obj=litellm.ModelResponse(id="chatcmpl-forged-router-meta", choices=[], usage=litellm.Usage()),
+        start_time=datetime.datetime.now(timezone.utc),
+        end_time=datetime.datetime.now(timezone.utc),
+    )
+    metadata = json.loads(payload["metadata"])
+    assert metadata["router_metadata"] is None
diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
index b96d2eb5322..bb59e576568 100644
--- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
+++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
@@ -124,6 +124,25 @@ class TestLiteLLMCompletionResponsesConfig:
         assert "extra_field" not in result["file"]
         assert "another_field" not in result["file"]
 
+    def test_transform_input_file_item_to_file_item_keeps_filename(self):
+        """OpenAI rejects file_data with no filename beside it, so dropping it 400s the request"""
+        result = (
+            LiteLLMCompletionResponsesConfig._transform_input_file_item_to_file_item(
+                {
+                    "type": "input_file",
+                    "filename": "report.pdf",
+                    "file_data": "data:application/pdf;base64,JVBERi0=",
+                }
+            )
+        )
+        assert result == {
+            "type": "file",
+            "file": {
+                "file_data": "data:application/pdf;base64,JVBERi0=",
+                "filename": "report.pdf",
+            },
+        }
+
     def test_transform_input_file_item_to_file_item_with_file_url(self):
         """file_url should be mapped to file_id for downstream URL handling"""
         result = (
diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts
index 8a564a07489..6f6c3165e4b 100644
--- a/ui/litellm-dashboard/src/lib/http/schema.d.ts
+++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts
@@ -9700,6 +9700,37 @@ export interface paths {
         patch?: never;
         trace?: never;
     };
+    "/openai/v1/responses/input_tokens": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Responses Input Tokens
+         * @description Count the input tokens of a Responses API request without calling the model.
+         *
+         *     Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens
+         *
+         *     ```bash
+         *     curl -X POST http://localhost:4000/v1/responses/input_tokens     -H "Content-Type: application/json"     -H "Authorization: Bearer sk-1234"     -d '{
+         *         "model": "gpt-4o",
+         *         "input": "Hello, how are you?"
+         *     }'
+         *     ```
+         *
+         *     Returns: `{"object": "response.input_tokens", "input_tokens": <count>}`
+         */
+        post: operations["responses_input_tokens_openai_v1_responses_input_tokens_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
     "/openai/v1/responses/{response_id}": {
         parameters: {
             query?: never;
@@ -12619,6 +12650,37 @@ export interface paths {
         patch?: never;
         trace?: never;
     };
+    "/responses/input_tokens": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Responses Input Tokens
+         * @description Count the input tokens of a Responses API request without calling the model.
+         *
+         *     Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens
+         *
+         *     ```bash
+         *     curl -X POST http://localhost:4000/v1/responses/input_tokens     -H "Content-Type: application/json"     -H "Authorization: Bearer sk-1234"     -d '{
+         *         "model": "gpt-4o",
+         *         "input": "Hello, how are you?"
+         *     }'
+         *     ```
+         *
+         *     Returns: `{"object": "response.input_tokens", "input_tokens": <count>}`
+         */
+        post: operations["responses_input_tokens_responses_input_tokens_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
     "/responses/{response_id}": {
         parameters: {
             query?: never;
@@ -15505,7 +15567,7 @@ export interface paths {
          *     Use this if if you want different teams to have different success/failure callbacks
          *
          *     Parameters:
-         *     - callback_name (Literal["langfuse", "langsmith", "gcs"], required): The name of the callback to add
+         *     - callback_name (str, required): The name of the callback to add, e.g. "langfuse", "langsmith", "gcs", "newrelic". The value is validated against the callbacks that support team-scoped credentials
          *     - callback_type (Literal["success", "failure", "success_and_failure"], required): The type of callback to add. One of:
          *         - "success": Callback for successful LLM calls
          *         - "failure": Callback for failed LLM calls
@@ -15521,6 +15583,8 @@ export interface paths {
          *         - langsmith_api_key: The API key for the Langsmith callback
          *         - langsmith_project: The project for the Langsmith callback
          *         - langsmith_base_url: The base URL for the Langsmith callback
+         *         - newrelic_api_key: The ingest license key for the team's New Relic account; routes both LLM/agent traces and cost metrics to that account. Requires the proxy to run with LITELLM_OTEL_V2=true, otherwise this callback is rejected with a 400
+         *         - newrelic_region: The New Relic region for the team's account ("us" or "eu"), riding the team's own key
          *
          *     Example curl:
          *     ```
@@ -16210,6 +16274,11 @@ export interface paths {
          *
          *     Meant to optimize querying spend data for analytics for a user.
          *
+         *     Reads daily spend records that only ever accumulate and are never affected by budget
+         *     resets. Their total can legitimately exceed the `spend` field returned by
+         *     `/v2/user/info`, which is a running budget counter that every budget reset sets back
+         *     to zero (or to the overage above `max_budget` when `budget_rollover` is enabled).
+         *
          *     Returns:
          *     (by date)
          *     - spend
@@ -16241,6 +16310,11 @@ export interface paths {
          * Get User Daily Activity Aggregated
          * @description Aggregated analytics for a user's daily activity without pagination.
          *     Returns the same response shape as the paginated endpoint with page metadata set to single-page.
+         *
+         *     Reads daily spend records that only ever accumulate and are never affected by budget
+         *     resets. Their total can legitimately exceed the `spend` field returned by
+         *     `/v2/user/info`, which is a running budget counter that every budget reset sets back
+         *     to zero (or to the overage above `max_budget` when `budget_rollover` is enabled).
          */
         get: operations["get_user_daily_activity_aggregated_user_daily_activity_aggregated_get"];
         put?: never;
@@ -19182,6 +19256,37 @@ export interface paths {
         patch?: never;
         trace?: never;
     };
+    "/v1/responses/input_tokens": {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        get?: never;
+        put?: never;
+        /**
+         * Responses Input Tokens
+         * @description Count the input tokens of a Responses API request without calling the model.
+         *
+         *     Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens
+         *
+         *     ```bash
+         *     curl -X POST http://localhost:4000/v1/responses/input_tokens     -H "Content-Type: application/json"     -H "Authorization: Bearer sk-1234"     -d '{
+         *         "model": "gpt-4o",
+         *         "input": "Hello, how are you?"
+         *     }'
+         *     ```
+         *
+         *     Returns: `{"object": "response.input_tokens", "input_tokens": <count>}`
+         */
+        post: operations["responses_input_tokens_v1_responses_input_tokens_post"];
+        delete?: never;
+        options?: never;
+        head?: never;
+        patch?: never;
+        trace?: never;
+    };
     "/v1/responses/{response_id}": {
         parameters: {
             query?: never;
@@ -21004,6 +21109,14 @@ export interface paths {
          *     This is the v2 replacement for /user/info, designed to avoid the "god endpoint" problem
          *     where the old endpoint loaded all keys and teams into memory.
          *
+         *     Note on `spend`: this is the user's running budget counter, which the budget reset job
+         *     resets whenever `budget_reset_at` elapses (see `budget_duration`): to zero by default,
+         *     or to the overage above `max_budget` when `budget_rollover` is enabled. It is NOT
+         *     lifetime or per-period historical spend. For historical spend over a date range, use
+         *     `/user/daily/activity` or `/user/daily/activity/aggregated`, which read daily spend
+         *     records that only ever accumulate and are never reset. The two values are expected to
+         *     diverge once a budget reset has occurred within the queried period.
+         *
          *     Access control:
          *     - Proxy admins can query any user
          *     - Team admins can query users within their teams
@@ -38418,6 +38531,8 @@ export interface components {
             input_cost_per_character?: number | null;
             /** Input Cost Per Token */
             input_cost_per_token?: number | null;
+            /** Internal Router Model */
+            internal_router_model?: boolean | null;
             /** Output Cost Per Character */
             output_cost_per_character?: number | null;
             /** Output Cost Per Token */
@@ -51474,6 +51589,26 @@ export interface operations {
             };
         };
     };
+    responses_input_tokens_openai_v1_responses_input_tokens_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": unknown;
+                };
+            };
+        };
+    };
     get_response_openai_v1_responses__response_id__get: {
         parameters: {
             query?: never;
@@ -54438,6 +54573,26 @@ export interface operations {
             };
         };
     };
+    responses_input_tokens_responses_input_tokens_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": unknown;
+                };
+            };
+        };
+    };
     get_response_responses__response_id__get: {
         parameters: {
             query?: never;
@@ -62860,6 +63015,26 @@ export interface operations {
             };
         };
     };
+    responses_input_tokens_v1_responses_input_tokens_post: {
+        parameters: {
+            query?: never;
+            header?: never;
+            path?: never;
+            cookie?: never;
+        };
+        requestBody?: never;
+        responses: {
+            /** @description Successful Response */
+            200: {
+                headers: {
+                    [name: string]: unknown;
+                };
+                content: {
+                    "application/json": unknown;
+                };
+            };
+        };
+    };
     get_response_v1_responses__response_id__get: {
         parameters: {
             query?: never;