mistral-small-latest now points to Small 3.2 (since June 2025).
Updated pricing from $0.10/$0.30 to $0.06/$0.18 per 1M tokens,
context from 32k to 131k, and added vision support to match
mistral-small-3-2-2506.
Add 9 new Mistral models (mistral-large-2512, mistral-medium-3-1-2508,
mistral-small-3-2-2506, ministral-3-3b/8b/14b-2512, saba-2502,
magistral-medium/small-1-2-2509) and update mistral-large-latest,
mistral-large-3, and mistral-medium-latest with correct pricing and
context windows.
Fixes#22585
Fixes#22591 - These models were missing from the pricing JSON, causing
$0 cost tracking when routed via the dashscope/* wildcard.
Pricing sourced from official Alibaba Cloud Model Studio docs (international tier).
Bedrock Claude models were missing cache_read_input_token_cost and
cache_creation_input_token_cost fields, causing cache tokens to be
billed at the full input rate instead of the discounted cache rate.
Added pricing using Bedrock's documented multipliers (0.1x for cache
read, 1.25x for cache write) consistent with all existing entries.
The `gemini/gemini-2.5-flash-image` entry had `litellm_provider` set to
`vertex_ai-language-models` instead of `gemini`. This causes a provider
mismatch in `_check_provider_match()` when the model is used via the
Gemini API provider (`custom_llm_provider="gemini"`), resulting in a
noisy error log on every request:
"This model isn't mapped yet. model=gemini/gemini-2.5-flash-image,
custom_llm_provider=gemini"
The `vertex_ai/gemini-2.5-flash-image` entry already exists with the
correct `vertex_ai-language-models` provider, and the sibling
`gemini/gemini-2.5-flash-image-preview` entry correctly uses `gemini`.
Resolve conflict in model_prices_and_context_window.json by keeping both
the new minimax-m2.5 model from upstream and the OpenRouter native models
from this branch.
Remove /v1/chat/completions and /v1/responses from supported_endpoints
and revert the audio model detection change since gpt-realtime-1.5 does
not go through Chat Completions.
New OpenAI realtime model released 2026-02-23. Adds pricing and capability
metadata for gpt-realtime-1.5 (32K context, 4K output, audio/image/text I/O).
Unlike gpt-realtime, this model also supports Chat Completions and Responses
endpoints (not just WebSocket).
Closes#22266
- add gemini-3.1-flash-image-preview + vertex_ai alias entries\n- set pricing to Gemini 3.1 Flash Image Preview rates\n- mirror updates in packaged backup model map\n- update llm cost calc regression test to cover new model
* Adjust input and output cost per token for mistral-small-2503
Cost per million for mistral-small-2503 is not correct.
In Azure Documentation:
Pay-as-you-go (per 1,000 tokens)
$0.0001
Model input
$0.0003
Model output
* Update input and output cost per token for model
* auth_with_role_name add region_name arg for cross-account sts
* update tests to include case with aws_region_name for _auth_with_aws_role
* Only pass region_name to STS client when aws_region_name is set
* Add optional aws_sts_endpoint to _auth_with_aws_role
* Parametrize ambient-credentials test for no opts, region_name, and aws_sts_endpoint
* consistently passing region and endpoint args into explicit credentials irsa
* fix env var leakage
* fix: bedrock openai-compatible imported-model should also have model arn encoded
* feat: show proxy url in ModelHub (#21660)
* fix(bedrock): correct modelInput format for Converse API batch models (#21656)
* fix(proxy): add model_ids param to access group endpoints for precise deployment tagging (#21655)
POST /access_group/new and PUT /access_group/{name}/update now accept an
optional model_ids list that targets specific deployments by their unique
model_id, instead of tagging every deployment that shares a model_name.
When model_ids is provided it takes priority over model_names, giving
API callers the same single-deployment precision that the UI already has
via PATCH /model/{model_id}/update.
Backward compatible: model_names continues to work as before.
Closes#21544
* feat(proxy): add custom favicon support\n\nAdd ability to configure a custom favicon for the litellm proxy UI.\n\n- Add favicon_url field to UIThemeConfig model\n- Add LITELLM_FAVICON_URL env var support\n- Add /get_favicon endpoint to serve custom favicons\n- Update ThemeContext to dynamically set favicon\n- Add favicon URL input to UI theme settings page\n- Add comprehensive tests\n\nCloses #8323 (#21653)
* fix(bedrock): prevent double UUID in create_file S3 key (#21650)
In create_file for Bedrock, get_complete_file_url is called twice:
once in the sync handler (generating UUID-1 for api_base) and once
inside transform_create_file_request (generating UUID-2 for the
actual S3 upload). The Bedrock provider correctly writes UUID-2 into
litellm_params["upload_url"], but the sync handler unconditionally
overwrites it with api_base (UUID-1). This causes the returned
file_id to point to a non-existent S3 key.
Fix: only set upload_url to api_base when transform_create_file_request
has not already set it, preserving the Bedrock provider's value.
Closes#21546
* feat(semantic-cache): support configurable vector dimensions for Qdrant (#21649)
Add vector_size parameter to QdrantSemanticCache and expose it through
the Cache facade as qdrant_semantic_cache_vector_size. This allows users
to use embedding models with dimensions other than the default 1536,
enabling cheaper/stronger models like Stella (1024d), bge-en-icl (4096d),
voyage, cohere, etc.
The parameter defaults to QDRANT_VECTOR_SIZE (env var or 1536) for
backward compatibility. When creating new collections, the configured
vector_size is used instead of the hardcoded constant.
Closes#9377
* fix(utils): normalize camelCase thinking param keys to snake_case (#21762)
Clients like OpenCode's @ai-sdk/openai-compatible send budgetTokens
(camelCase) instead of budget_tokens in the thinking parameter, causing
validation errors. Add early normalization in completion().
* feat: add optional digest mode for Slack alert types (#21683)
Adds per-alert-type digest mode that aggregates duplicate alerts
within a configurable time window and emits a single summary message
with count, start/end timestamps.
Configuration via general_settings.alert_type_config:
alert_type_config:
llm_requests_hanging:
digest: true
digest_interval: 86400
Digest key: (alert_type, request_model, api_base)
Default interval: 24 hours
Window type: fixed interval
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: add blog_posts.json and local backup
* feat: add GetBlogPosts utility with GitHub fetch and local fallback
Adds GetBlogPosts class that fetches blog posts from GitHub with a 1-hour
in-process TTL cache, validates the response, and falls back to the bundled
blog_posts_backup.json on any network or validation failure.
* test: add cache reset fixture and LITELLM_LOCAL_BLOG_POSTS test
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add GET /public/litellm_blog_posts endpoint
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: log fallback warning in blog posts endpoint and tighten test
* feat: add disable_show_blog to UISettings
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add useUISettings and useDisableShowBlog hooks
* fix: rename useUISettings to useUISettingsFlags to avoid naming collision
* fix: use existing useUISettings hook in useDisableShowBlog to avoid cache duplication
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add BlogDropdown component with react-query and error/retry state
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: enforce 5-post limit in BlogDropdown and add cap test
* fix: add retry, stable post key, enabled guard in BlogDropdown
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add BlogDropdown to navbar after Docs link
* feat: add network_mock transport for benchmarking proxy overhead without real API calls
Intercepts at httpx transport layer so the full proxy path (auth, routing,
OpenAI SDK, response transformation) is exercised with zero-latency responses.
Activated via `litellm_settings: { network_mock: true }` in proxy config.
* Litellm dev 02 19 2026 p2 (#21871)
* feat(ui/): new guardrails monitor 'demo
mock representation of what guardrails monitor looks like
* fix: ui updates
* style(ui/): fix styling
* feat: enable running ai monitor on individual guardrails
* feat: add backend logic for guardrail monitoring
* fix(guardrails/usage_endpoints.py): fix usage dashboard
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo (#21754)
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo
* fix(budget): update stale docstring on get_budget_reset_time
* fix: add missing return type annotations to iterator protocol methods in streaming_handler (#21750)
* fix: add return type annotations to iterator protocol methods in streaming_handler
Add missing return type annotations to __iter__, __aiter__, __next__, and __anext__ methods in CustomStreamWrapper and related classes.
- __iter__(self) -> Iterator["ModelResponseStream"]
- __aiter__(self) -> AsyncIterator["ModelResponseStream"]
- __next__(self) -> "ModelResponseStream"
- __anext__(self) -> "ModelResponseStream"
Also adds AsyncIterator and Iterator to typing imports.
Fixes issue with PLR0915 noqa comments and ensures proper type checking support.
Related to: BerriAI/litellm#8304
* fix: add ruff PLR0915 noqa for files with too many statements
* Add gollem Go agent framework cookbook example (#21747)
Show how to use gollem, a production Go agent framework, with
LiteLLM proxy for multi-provider LLM access including tool use
and streaming.
* fix: avoid mutating caller-owned dicts in SpendUpdateQueue aggregation (#21742)
* fix(vertex_ai): enable context-1m-2025-08-07 beta header (#21870)
* server root path regression doc
* fixing syntax
* fix: replace Zapier webhook with Google Form for survey submission (#21621)
* Replace Zapier webhook with Google Form for survey submission
* Add back error logging for survey submission debugging
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* Revert "Merge pull request #21140 from BerriAI/litellm_perf_user_api_key_auth"
This reverts commit 0e1db3f7e4, reversing
changes made to 7e2d6f2355.
* test_vertex_ai_gemini_2_5_pro_streaming
* UI new build
* fix rendering
* ui new build
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* release note docs
* docs
* adding image
* fix(vertex_ai): enable context-1m-2025-08-07 beta header
The `context-1m-2025-08-07` Anthropic beta header was set to `null` for vertex_ai,
causing it to be filtered out when users set `extra_headers: {anthropic-beta: context-1m-2025-08-07}`.
This prevented using Claude's 1M context window feature via Vertex AI, resulting in
`prompt is too long: 460500 tokens > 200000 maximum` errors.
Fixes#21861
---------
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* Revert "fix(vertex_ai): enable context-1m-2025-08-07 beta header (#21870)" (#21876)
This reverts commit bce078a796.
* docs(ui): add pre-PR checklist to UI contributing guide
Add testing and build verification steps per maintainer feedback
from @yjiang-litellm. Contributors should run their related tests
per-file and ensure npm run build passes before opening PRs.
* Fix entries with fast and us/
* Add tests for fast and us
* Add support for Priority PayGo for vertex ai and gemini
* Add model pricing
* fix: ensure arrival_time is set before calculating queue time
* Fix: Anthropic model wildcard access issue
* Add incident report
* Add ability to see which model cost map is getting used
* Fix name of title
* Readd tpm limit
* State management fixes for CheckBatchCost
* Fix PR review comments
* State management fixes for CheckBatchCost - Address greptile comments
* fix mypy issues:
* Add Noma guardrails v2 based on custom guardrails (#21400)
* Fix code qa issues
* Fix mypy issues
* Fix mypy issues
* Fix test_aaamodel_prices_and_context_window_json_is_valid
* fix: update calendly on repo
* fix(tests): use counter-based mock for time.time in prisma self-heal test
The test used a fixed side_effect list for time.time(), but the number
of calls varies by Python version, causing StopIteration on 3.12 and
AssertionError on 3.14. Replace with an infinite counter-based callable
and assert the timestamp was updated rather than checking for an exact
value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(tests): use absolute path for model_prices JSON in validation test
The test used a relative path 'litellm/model_prices_and_context_window.json'
which only works when pytest runs from a specific working directory.
Use os.path based on __file__ to resolve the path reliably.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update tests/test_litellm/test_utils.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(tests): use os.path instead of Path to avoid NameError
Path is not imported at module level. Use os.path.join which is already
available.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* clean up mock transport: remove streaming, add defensive parsing
* docs: add Google GenAI SDK tutorial (JS & Python) (#21885)
* docs: add Google GenAI SDK tutorial for JS and Python
Add tutorial for using Google's official GenAI SDK (@google/genai for JS,
google-genai for Python) with LiteLLM proxy. Covers pass-through and
native router endpoints, streaming, multi-turn chat, and multi-provider
routing via model_group_alias. Also updates pass-through docs to use the
new SDK replacing the deprecated @google/generative-ai.
* fix(docs): correct Python SDK env var name in GenAI tutorial
GOOGLE_GENAI_API_KEY does not exist in the google-genai SDK.
The correct env var is GEMINI_API_KEY (or GOOGLE_API_KEY).
Also note that the Python SDK has no base URL env var.
* fix(docs): replace non-existent GOOGLE_GENAI_BASE_URL env var in interactions.md
The Python google-genai SDK does not read GOOGLE_GENAI_BASE_URL.
Use http_options={"base_url": "..."} in code instead.
* docs: add network mock benchmarking section
* docs: tweak benchmarks wording
* fix: add auth headers and empty latencies guard to benchmark script
* refactor: use method-level import for MockOpenAITransport
* fix: guard print_aggregate against empty latencies
* fix: add INCOMPLETE status to Interactions API enum and test
Google added INCOMPLETE to the Interactions API OpenAPI spec status enum.
Update both the Status3 enum in the SDK types and the test's expected
values to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Guardrail Monitor - measure guardrail reliability in prod (#21944)
* fix: fix log viewer for guardrail monitoring
* feat(ui/): fix rendering logs per guardrail
* fix: fix viewing logs on overview tab of guardrail
* fix: log viewer
* fix: fix naming to align with metric
* docs: add performance & reliability section to v1.81.14 release notes
* fix(tests): make RPM limit test sequential to avoid race condition
Concurrent requests via run_in_executor + asyncio.gather caused a race
condition where more requests slipped through the rate limiter than
expected, leading to flaky test failures (e.g. 3 successes instead of 2
with rpm_limit=2).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: Singapore guardrail policies (PDPA + MAS AI Risk Management) (#21948)
* feat: Singapore PDPA PII protection guardrail policy template
Add Singapore Personal Data Protection Act (PDPA) guardrail support:
Regex patterns (patterns.json):
- sg_nric: NRIC/FIN detection ([STFGM] + 7 digits + checksum letter)
- sg_phone: Singapore phone numbers (+65/0065/65 prefix)
- sg_postal_code: 6-digit postal codes (contextual)
- passport_singapore: Passport numbers (E/K + 7 digits, contextual)
- sg_uen: Unique Entity Numbers (3 formats)
- sg_bank_account: Bank account numbers (dash format, contextual)
YAML policy templates (5 sub-guardrails):
- sg_pdpa_personal_identifiers: s.13 Consent
- sg_pdpa_sensitive_data: Advisory Guidelines
- sg_pdpa_do_not_call: Part IX DNC Registry
- sg_pdpa_data_transfer: s.26 overseas transfers
- sg_pdpa_profiling_automated_decisions: Model AI Governance Framework
Policy template entry in policy_templates.json with 9 guardrail definitions
(4 regex-based + 5 YAML conditional keyword matching).
Tests:
- test_sg_patterns.py: regex pattern unit tests
- test_sg_pdpa_guardrails.py: conditional keyword matching tests (100+ cases)
* feat: MAS AI Risk Management Guidelines guardrail policy template
Add Monetary Authority of Singapore (MAS) AI Risk Management Guidelines
guardrail support for financial institutions:
YAML policy templates (5 sub-guardrails):
- sg_mas_fairness_bias: Blocks discriminatory financial AI (credit/loans/insurance by protected attributes)
- sg_mas_transparency_explainability: Blocks opaque/unexplainable AI for consequential financial decisions
- sg_mas_human_oversight: Blocks fully automated financial decisions without human-in-the-loop
- sg_mas_data_governance: Blocks unauthorized sharing/mishandling of financial customer data
- sg_mas_model_security: Blocks adversarial attacks, model poisoning, inversion on financial AI
Policy template entry in policy_templates.json with 5 guardrail definitions.
Aligned with MAS FEAT Principles, Project MindForge, and NIST AI RMF.
Tests:
- test_sg_mas_ai_guardrails.py: conditional keyword matching tests (100+ cases)
* fix: address SG pattern review feedback
- Update NRIC lowercase test for IGNORECASE runtime behavior
- Add keyword context guard to sg_uen pattern to reduce false positives
* docs: clarify MAS AIRM timeline references
- Explicitly mark MAS AIRM as Nov 2025 consultation draft
- Add 2018 qualifier for FEAT principles in MAS policy descriptions
- Update MAS guardrail wording to avoid release-year ambiguity
* chore: commit resolved MAS policy conflicts
* test:
* chore:
* Add OpenAI Agents SDK tutorial with LiteLLM Proxy to docs (#21221)
* Add OpenAI Agents SDK tutorial to docs
* Update OpenAI Agents SDK tutorial to use LiteLLM environment variables
* Enhance OpenAI Agents SDK tutorial with built-in LiteLLM extension details and updated configuration steps. Adjust section headings for clarity and improve the flow of information regarding model setup and usage.
* adjust blog posts to fetch from github first
* feat(videos): add variant parameter to video content download (#21955)
openai videos models support the features to download variants.
See more details here: https://developers.openai.com/api/docs/guides/video-generation#use-image-references.
Plumb variant (e.g. "thumbnail", "spritesheet") through the full
video content download chain: avideo_content → video_content →
video_content_handler → transform_video_content_request. OpenAI
appends ?variant=<value> to the GET URL; other providers accept
the parameter in their signature but ignore it.
* fixing path
* adjust blog post path
* Revert duplicate issue checker to text-based matching, remove duplicate PR workflow
Remove the Claude Code-powered duplicate PR detection workflow and revert
the duplicate issue checker back to wow-actions/potential-duplicates with
text similarity matching.
* ui changes
* adding tests
* adjust default aggregation threshold
* fix(videos): pass api_key from litellm_params to video remix handlers (#21965)
video_remix_handler and async_video_remix_handler were not falling back
to litellm_params.api_key when the api_key parameter was None, causing
Authorization: Bearer None to be sent to the provider. This matches the
pattern already used by async_video_generation_handler.
* adding testing coverage + fixing flaky tests
* fix(ollama): thread api_base through get_model_info and add graceful fallback
When users pass api_base to litellm.completion() for Ollama, the model
info fetch (context window, function_calling support) was ignoring the
user's api_base and only reading OLLAMA_API_BASE env var or defaulting
to localhost:11434. This caused confusing errors in logs when Ollama
runs on a remote server.
Thread api_base from litellm_params through the get_model_info call
chain so OllamaConfig.get_model_info() uses the correct server. Also
return safe defaults instead of raising when the server is unreachable.
Fixes#21967
---------
Co-authored-by: An Tang <ta@stripe.com>
Co-authored-by: janfrederickk <75388864+janfrederickk@users.noreply.github.com>
Co-authored-by: Zhenting Huang <3061613175@qq.com>
Co-authored-by: Darien Kindlund <darien@kindlund.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: Ryan Crabbe <rcrabbe@berkeley.edu>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: LeeJuOh <56071126+LeeJuOh@users.noreply.github.com>
Co-authored-by: Monesh Ram <31161039+WhoisMonesh@users.noreply.github.com>
Co-authored-by: Trevor Prater <trevor.prater@gmail.com>
Co-authored-by: The Mavik <179817126+themavik@users.noreply.github.com>
Co-authored-by: Edwin Isac <33712823+edwiniac@users.noreply.github.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: Harshit Jain <harshitjain0562@gmail.com>
Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com>
Co-authored-by: Ephrim Stanley <ephrim.stanley@point72.com>
Co-authored-by: TomAlon <tom@noma.security>
Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com>
Co-authored-by: Ron Zhong <ron-zhong@hotmail.com>
Co-authored-by: Arindam Majumder <109217591+Arindam200@users.noreply.github.com>
Co-authored-by: Lei Nie <lenie@quora.com>
- Rename "Agentic Research API" to "Agent API"
Expand
- supported Responses API parameters
- Fix function
tool handling to pass custom function tools through unchanged instead of heuristically mapping them.
-Update model registry with current Perplexity
models and presets
- Add Function Calling and Structured Outputs documentation sections.
- Unit tests for transformation logic.
* fix(model-pricing): add missing fireworks_ai model pricing for glm-4p7, minimax-m2p1, kimi-k2p5
Fireworks AI models called via short-form (fireworks_ai/<model>) were
reporting $0.00 cost because the pricing JSON lacked short-form entries.
The lookup fell through to the fireworks-ai-default bucket which has
zero cost.
Added 5 new entries to model_prices_and_context_window.json:
- fireworks_ai/accounts/fireworks/models/glm-4p7 (new long-form)
- fireworks_ai/accounts/fireworks/models/minimax-m2p1 (new long-form)
- fireworks_ai/glm-4p7 (new short-form)
- fireworks_ai/minimax-m2p1 (new short-form)
- fireworks_ai/kimi-k2p5 (new short-form; long-form already existed)
Pricing sourced from fireworks.ai model pages and pricing page.
* add cache_read_input_token_cost to kimi-k2p5 long-form entry for consistency
The Apertus 8B and 70B models do not support standard OpenAI-style
tool calling. Per Swiss AI's docs, tool use integration into inference
engines is still in development. Set supports_function_calling and
supports_tool_choice to false.
Fixes#21124
