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* default requested_model to empty string on litellm-side rejects * Update litellm/router.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix: scope key access_group_ids override by team's assigned groups A team member could set any access_group_ids on their key (e.g. a group assigned only to a different team) and override the team's model restriction. Intersect the key's access_group_ids with team_object.access_group_ids in _key_access_group_grants_model so foreign groups are dropped before model expansion. Adds a regression test that asserts expansion is never called for foreign groups. * [Fix] Proxy: Skip Personal Budget Hook When Reservation Covers Counter The reservation path (PR #26845) atomically pre-fills `spend:user:{user_id}` and admits at the strict-`<` boundary. The legacy `_PROXY_MaxBudgetLimiter` pre-call hook re-reads the same counter with `>=`, so a reservation that fills the counter to exactly `max_budget` (e.g. a request without a `max_tokens` cap that falls back to reserving the smallest remaining headroom) is rejected by the hook even though the reservation already admitted it. Skip the hook when the request's active `budget_reservation` covers `spend:user:{user_id}`. The reservation is the source of truth for that counter cross-pod; the legacy `>=` path remains in place for requests without a reservation (e.g. paths that bypass the reservation entirely). Reproduces as `tests/otel_tests/test_prometheus.py::test_user_budget_metrics` on a fresh user with `max_budget=10` calling `fake-openai-endpoint` without `max_tokens`. Adds focused unit coverage in `tests/test_litellm/proxy/hooks/test_max_budget_limiter.py`. * harden bedrock file bucket validation * Fix syntax errors from botched merge in router.py * Fix Vertex batch output edge cases * [Fix] RBAC: Drop management_routes Write Fallback for Admin Viewer Greptile P1: the unsafe-method branch of `_check_proxy_admin_viewer_access` ended with a blanket `if route in management_routes: return`. That set is a mix of reads (info/list — handled via the safe-method GET branch above) and writes. The fallback let Admin Viewer POST to write endpoints not enumerated in `_ADMIN_VIEWER_BLOCKED_WRITE_ROUTES`, including: - /team/block, /team/unblock, /team/permissions_update - /jwt/key/mapping/{new,update,delete} - /key/bulk_update - /key/{key_id}/reset_spend Remove the fallback. The two remaining allow sets (admin_viewer_routes and global_spend_tracking_routes) are both read-only, so removal does not affect the legitimate POST-as-read cases (e.g. /spend/calculate, which is in spend_tracking_routes ⊂ admin_viewer_routes). Tests: - 8 new parametrized cases pinning each previously-leaking management write endpoint to 403 on POST for PROXY_ADMIN_VIEW_ONLY. * fix(tests): anchor VCR redis cassette key to repo root `os.path.relpath` with no `start` arg uses the current working directory, so running pytest from a subdirectory produced a different Redis key than running from the repo root. CI-recorded cassettes and locally-replayed runs would silently miss each other's cache. Anchor the path to the repo root (derived from `__file__`) so the key is stable regardless of CWD. https://claude.ai/code/session_018uCx7pcrkdUJZrCVMaTdPx * fix: gate key access_group override on group's own assignment Replaces the previous intersect-with-team.access_group_ids check, which made the override unreachable in practice (the team-gate fallback already covered every case the intersection allowed). The override now resolves each of the key's access_group_ids via get_access_object and accepts the group only if its assigned_team_ids includes the key's team_id, or its assigned_key_ids includes the key's token. This fulfills the original ask (a key can extend a team's allow-list via a group the admin granted to that team or that specific key) while still rejecting foreign groups referenced by team members of other teams. * [Fix] Proxy/Key Management: Honor team_member_permissions /key/list In /key/list Endpoint When a team grants /key/list via team_member_permissions, non-admin members should see all keys for that team — same as a team admin. Previously the classification in list_keys() only checked admin status, so permitted members fell into the service-account-only path and could not see other members' personal keys. Routes those members into the full-visibility set. * Fix access-group bypass via litellm-model fallback path When _get_all_deployments returns 0 candidates and the litellm-model fallback branch (_get_deployment_by_litellm_model) finds deployments that the access-group filter then empties, _access_group_filter_emptied_candidates remained False (it was captured before that branch ran). The router would then proceed to default fallbacks; the fallback model could have no access_groups and short-circuit the filter, silently serving a caller blocked by access-group restrictions. Update the flag inside the litellm-model branch when filtering empties a non-empty candidate set so the default-fallback guard still triggers. * fix(proxy): redact MCP server URL and headers for non-admin viewers (VERIA-8) Many MCP integrations (Zapier, etc.) embed an upstream API key directly in the server URL, e.g. ``https://actions.zapier.com/mcp/<api-key>/sse``. The list and single-server endpoints were returning the full URL to any authenticated user — `_redact_mcp_credentials` only stripped the explicit ``credentials`` field, and `_sanitize_mcp_server_for_virtual_key` only ran for restricted virtual keys. Non-admin internal users could read the dashboard, click the unmask toggle, and exfiltrate the raw token. Add `_sanitize_mcp_server_for_non_admin` that runs on top of the existing credential redaction and clears the credential-bearing fields: - ``url`` (the primary leak vector) - ``spec_path`` (OpenAPI spec URLs that may carry tokens) - ``static_headers`` / ``extra_headers`` (Authorization) - ``env`` (arbitrary secrets) - ``authorization_url`` / ``token_url`` / ``registration_url`` Identity fields (``server_id``, ``alias``, ``mcp_info``, etc.) are preserved so the UI can still list servers a non-admin's team has access to. Apply the new sanitizer in `fetch_all_mcp_servers` and the per-server fetch path right after the existing virtual-key branch. Update the existing `test_list_mcp_servers_non_admin_user_filtered` assertions that previously checked URL visibility. Frontend defense-in-depth: hide the URL unmask toggle on `mcp_server_view.tsx` unless the viewer is a proxy admin. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Fix runtime policy attachment initialization Mark runtime-created policies and attachments initialized so global policy attachments created from the policy builder apply immediately without requiring a restart. Co-authored-by: Cursor <cursoragent@cursor.com> * test(router): cover _try_early_resolve_deployments_for_model_not_in_names The router_code_coverage CI check requires every function in router.py to be referenced by at least one test under tests/{local_testing, router_unit_tests,test_litellm} in a file with "router" in its name. The recently-extracted helper had no direct test, so the check failed with "0.45% of functions in router.py are not tested". Add a focused test that exercises the four return paths: model already in self.model_names, no fallback applies, pattern-router match, and default_deployment substitution (also asserting the stored default isn't mutated). https://claude.ai/code/session_019AVp1XL7RT9RxRe4qRLkay * Fix policy registry teardown in tests Reset the policy ID index during policy engine test cleanup so stale policy versions cannot leak between tests. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(batches): count non-chat tokens, validate batch-file model access (VERIA-39) (#27015) * fix(batches): count non-chat tokens and validate every model in batch file Two security control bypasses on POST /v1/batches: 1. `_get_batch_job_input_file_usage` only summed tokens for `body.messages` (chat completions). Embedding (`input`) and text completion (`prompt`) batches reported zero, letting massive non-chat workloads slip past TPM rate limits. Extend the counter to handle string and list shapes for both fields. 2. The batch input file was forwarded to the upstream provider without inspecting the models named inside the JSONL — only the outer `model` query parameter was checked against the caller's allowlist. A caller restricted to gpt-3.5 could submit a batch targeting gpt-4o and the upstream would execute it under the proxy's shared API key. Add `_get_models_from_batch_input_file_content` (returns the distinct `body.model` values) and call it from `_enforce_batch_file_model_access` in the pre-call hook, which runs each model through `can_key_call_model` so the same allowlist semantics (wildcards, access groups, all-proxy-models, team aliases) the proxy enforces on `/chat/completions` apply here too. Any unauthorized model raises a 403 before the file is forwarded. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(batches): count pre-tokenized prompt/input shapes, classify 403 logs Two follow-ups from the Greptile review on the batch validation PR: 1. P1 TPM bypass via integer token arrays. The OpenAI batch schema accepts ``prompt`` and ``input`` as ``list[int]`` (a single pre-tokenized prompt) or ``list[list[int]]`` (multiple) in addition to the string and ``list[str]`` shapes. Pre-fix only the string shapes were counted, so a caller could submit a batch with hundreds of millions of pre-tokenized tokens and the rate limiter would record zero. Extract the per-field logic into ``_count_prompt_or_input_tokens`` and count each int as one token. 2. P2 access-denial logs were indistinguishable from I/O failures. ``count_input_file_usage`` caught every exception under a generic "Error counting input file usage" message, so an intentional 403 from ``_enforce_batch_file_model_access`` looked the same in the logs as a missing file or a Prisma timeout. Catch ``HTTPException`` separately and log 403s at WARNING level with a security-relevant message before re-raising. Tests cover the new shapes: single ``list[int]``, ``list[list[int]]`` (the worst-case bypass vector), and embeddings ``input`` with pre-tokenized arrays. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(proxy): re-validate user_id after /user/info re-parses query (#27009) * fix(proxy): re-validate user_id ownership after /user/info re-parses query The route-level access check in `RouteChecks.non_proxy_admin_allowed_routes_check` reads `request.query_params.get("user_id")`, which decodes literal `+` to spaces. The endpoint then re-parses the raw query string with `urllib.unquote` in `get_user_id_from_request` to preserve `+` characters (so plus-addressed emails work as user_ids). Those two paths produce different ids: a caller who registered a user_id containing a literal space could pass the route check and then read another user's row by sending the encoded `+` form. Add `_enforce_user_info_access` and call it after `_normalize_user_info_user_id` returns the final id. Proxy admin / view-only admin still bypass; everyone else must match the resolved user_id (or have no user_id, which falls back to the caller's own id later in the handler). Tests cover the admin bypass, owner-match path, and the cross-user lookup that this change blocks. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(proxy): apply user_info ownership check to PROXY_ADMIN_VIEW_ONLY `_enforce_user_info_access` was bypassing both PROXY_ADMIN and PROXY_ADMIN_VIEW_ONLY, but the upstream route check in `RouteChecks.non_proxy_admin_allowed_routes_check` only treats PROXY_ADMIN as a true admin for the `/user/info` route — view-only admins go through the `user_id == valid_token.user_id` enforcement along with regular users. Mirroring that asymmetry left the same encoded-`+` bypass open for view-only admins whose user_id contains a literal space. Drop the PROXY_ADMIN_VIEW_ONLY exemption so the post-decode re-check matches the upstream rule. Update tests: a view-only admin must now be blocked from cross-user lookups but still allowed to read their own row. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(spend-logs): opt-in suppression of stack traces in spend-tracking error logs Adds LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS env var. When set to true and the proxy log level is INFO or above, spend-tracking error paths emit a single ERROR line without the full traceback. Stack traces are preserved at DEBUG and the Sentry / proxy_logging_obj.failure_handler path is unchanged. The new spend_log_error helper is wired through the spend write hot path: - DBSpendUpdateWriter (update_database, _update_*_db, batch upsert, redis-commit fallbacks) - _ProxyDBLogger._PROXY_track_cost_callback - get_logging_payload exception path - update_spend / update_daily_tag_spend / spend logs queue monitor Resolves LIT-2704. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(spend-logs): preserve no-traceback behavior for update_daily_tag_spend This call site previously logged a single-line error via verbose_proxy_logger.error() with no traceback. Switching it to spend_log_error(..., exc=e) caused a full stack trace to render by default (when LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS is unset), which contradicts the PR goal of leaving default behavior unchanged. Revert this specific site to the original error log call. * fix(spend-logs): preserve no-traceback behavior for update_daily_tag_spend Bugbot caught a regression: the previous error log here was a single-line verbose_proxy_logger.error(...) with no traceback. spend_log_error attaches the active exception's traceback by default (when the suppression env var is unset), so swapping it in changed default behavior. Revert this one site to its original .error() call to keep the PR strictly opt-in. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * feat(spend-logs): suppress traceback in SpendLogs error_information row Extend LITELLM_SUPPRESS_SPEND_LOG_TRACEBACKS to the failure callback so the per-row Metadata pane in the UI no longer shows the stack trace when the opt-in env var is set, matching the existing console-side suppression. https://claude.ai/code/session_014dztoRbRnRvq54HL9EyHx6 * [Fix] Proxy: Repair Merge Fallout In Router-Override Fallback Auth Conflict resolution for #26968 dropped the `Iterator` typing import (NameError at module load), left a dead `fallback_models = cast(...)` block, and the new tests called `_enforce_key_and_fallback_model_access` without the now-required `request` kwarg. * isolate dual OTEL handlers * harden cloud file compatibility path * harden cloud file compatibility path * [Fix] Proxy/Key Management: Align Key-Org Membership Checks On Generate And Regenerate Mirrors the membership rule on /key/update so that /key/generate and /key/{key}/regenerate apply the same `_validate_caller_can_assign_key_org` gate when the caller specifies an `organization_id`. Proxy admins bypass. The check no-ops when `organization_id` is not being set. * thread trusted params through vertex file content * trust only server legacy file flag * chore(proxy): keep public AI hub unauthenticated * fix(proxy): preserve low-detail readiness status * [Test] Anthropic: Replace Legacy Claude-4-Sonnet Alias With Haiku 4.5 Three live-API tests pinned to claude-4-sonnet-20250514, which is a non-canonical alias of claude-sonnet-4-20250514. Anthropic's main API no longer resolves the legacy form under freshly issued keys, so the tests fail with not_found_error. The token counter test pinned to claude-sonnet-4-20250514 itself (deprecation_date 2026-05-14, two weeks out) was on borrowed time too. Bump all four to claude-haiku-4-5-20251001 — capability superset for what these tests exercise (streaming, parallel tool calling, extended thinking, token counting), no upcoming deprecation, cheaper per-token. * chore(proxy): move URL-valued model/file_id guard from SDK to proxy The previous per-provider guards in HuggingFace, Oobabooga, and Gemini files lived in the SDK layer, breaking SDK callers who legitimately pass URL-valued model identifiers. Move the check to the proxy boundary in add_litellm_data_to_request so SDK users keep working while proxy users default-deny URL-valued model and file_id, with admin opt-in via litellm.provider_url_destination_allowed_hosts. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * [Chore] Proxy/UI: Drop stray _experimental/out/chat/index.html This file is a regenerable UI build artifact that should not be tracked in source. Removing so the merge into litellm_internal_staging stays clean. * [Test] Anthropic Passthrough: Bump Streaming Cost-Injection Test To Haiku 4.5 test_anthropic_messages_streaming_cost_injection hits the proxy's /v1/messages route, which routes via the anthropic/* wildcard to api.anthropic.com. The 404 surfaced in the test was Anthropic's own not_found_error propagated back through the proxy (visible from the x-litellm-model-id hash on the response — the proxy did route). Same root cause as the prior commit: the legacy claude-4-sonnet-20250514 alias is no longer recognized by Anthropic's main API under the new key. Swap to claude-haiku-4-5-20251001 — same routing path, canonical model. * fix(proxy): handle ownership-recording failures after upstream create If record_container_owner raises after the upstream container is created, the user previously got a 500 with no usable container — they were billed for an unreachable resource. Move ownership recording into the create path's exception handling and split the two failure modes: - HTTPException from the recorder (auth conflicts) propagates verbatim so the client sees the real status code, not a generic LLM error. - Unexpected exceptions are logged and swallowed; the response is returned to the caller so they aren't billed for a container they can't address. The DB row stays untracked until an operator reconciles. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(guardrails): close post-call coverage gaps * fix(types): add /team/permissions_bulk_update to management_routes The blocklist check in _check_proxy_admin_viewer_access only fires for routes that match LiteLLMRoutes.management_routes — the bulk-update endpoint was missing from that list, so the test for view-only admins on /team/permissions_bulk_update fell through to "allow." Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * [Test] Anthropic Passthrough: Bump Thinking Tests Off Legacy Sonnet 4 Alias base_anthropic_messages_test.test_anthropic_messages_with_thinking and test_anthropic_streaming_with_thinking still pinned to claude-4-sonnet-20250514 — the same legacy alias Anthropic no longer recognizes under freshly issued keys. The other four tests in this base class already use claude-sonnet-4-5-20250929; these two were missed. Bump to claude-haiku-4-5-20251001 (supports_reasoning=true, no upcoming deprecation). Subclasses including TestAnthropicPassthroughBasic inherit these methods. * fix(guardrails): cover multi-choice output variants * fix(proxy): preserve public ai hub ui setting * fix(scim): cascade FK cleanup on user delete and surface block status in UI SCIM DELETE /Users/{id} previously called litellm_usertable.delete without clearing rows that FK back to the user, so Postgres rejected the delete with LiteLLM_InvitationLink_user_id_fkey and the SCIM caller saw a 500. Add a helper to drop invitation_link, organization_membership, and team_membership rows before the user delete (mirrors /user/delete in internal_user_endpoints). Also add a Status column to the Virtual Keys and Internal Users tables so admins can see at a glance which keys are blocked and which users SCIM has deactivated. SCIM-blocked keys carry a tooltip explaining the origin. Pin the dashboard's Node version to 20 via .nvmrc to match CI. * chore: update Next.js build artifacts (2026-05-02 03:21 UTC, node v20.20.2) * perf(proxy): cache container/skill ownership reads on the hot path Container ownership and skill rows are looked up on every retrieve / delete / list / file-content / chat-completion-with-skill call. The new stores wrapped raw Prisma queries with no cache, putting one DB round-trip on each request. Add an in-process TTL'd cache mirroring the _byok_cred_cache pattern in mcp_server/server.py: per-key (value, monotonic_timestamp), 60s TTL, 10000-entry cap with full-clear on overflow, invalidated by every write. Negative results (`None`) are cached too so untracked-resource checks also skip the DB. Tests cover: cache-after-first-hit, negative caching, write invalidation, no-caching-on-DB-error, TTL expiry, capacity eviction. 56 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: update Next.js build artifacts (2026-05-02 03:39 UTC, node v20.20.2) * fix: remove traceback key instead of it being "" * fix: linting error * fix(scim): preserve scim_active on PUT when client omits the field A SCIM PUT may legally omit `active` (full-replace with the field absent). Pydantic fills the SCIMUser.active default of True, so the PUT handler was overwriting metadata.scim_active with True even when the client never sent it — silently reactivating a previously SCIM-blocked user and unblocking their keys. Use model_fields_set to detect whether the client actually sent `active`. If omitted, preserve the prior scim_active value and skip the cascade to virtual keys. Also drop comments added in this PR that just narrate what the code does; keep only the docstrings and the SQL-NULL pitfall note that explain non-obvious behaviour. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(proxy): use set lookup for permitted agent filters * fix(mcp): redact command fields for non-admin server views * fix(proxy): forward decoded container ids after ownership checks * fix(caching): handle stale isolated Redis semantic index * fix(cloudflare): support response_text in streaming chunk parser Newer Cloudflare Workers AI models (e.g. Nemotron) emit 'response_text' instead of 'response' on streamed chunks. The non-streaming path was already updated to fall back to 'response_text' (#26385), but the streaming chunk parser still only read 'response', which caused streaming requests against those models to silently produce empty content. Mirror the non-streaming fallback in CloudflareChatResponseIterator.chunk_parser and add a streaming test for the response_text shape. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * Fix code qa * Address bugbot: drop dead encode/decode helpers; preserve empty custom_id - Remove unused _encode_gcp_label_value / _decode_gcp_label_value singular helpers; only the _chunks variants are actually called. - Use 'is not None' check for custom_id so empty-string custom_ids are still labeled and round-trip through batch outputs. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * Forward Vertex file content logging context * test vertex file content logging forwarding Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> * Fix Vertex batch output logging mutation * fix: don't mutate caller's logging_obj in _try_transform_vertex_batch_output_to_openai The method was overwriting logging_obj.optional_params, logging_obj.model, and logging_obj.start_time on the caller's Logging instance. When invoked from llm_http_handler.py's generic framework path, the framework's own logging_obj (which already went through pre_call) had its properties clobbered, causing model and start_time to reflect the last batch line's values rather than the original call context. Fix: create a fresh local Logging instance for the per-line transformation instead of mutating the incoming logging_obj. The caller's object is now left entirely untouched regardless of whether a logging_obj was passed in or not. Regression tests added to verify model, start_time, and optional_params are not mutated on the caller's logging_obj. Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> * feat: add opt-out flag for Vertex batch output transformation Adds litellm.disable_vertex_batch_output_transformation (default False). When True, afile_content returns raw Vertex predictions.jsonl untouched so users that parse candidates/modelVersion directly are not broken. * fix(anthropic,bedrock): omit thinking/output_config when reasoning_effort="none" Setting reasoning_effort="none" on Anthropic chat models (direct, Bedrock Invoke, Bedrock Converse, Vertex AI Anthropic, Azure AI Anthropic) crashed LiteLLM with: litellm.APIConnectionError: 'NoneType' object has no attribute 'get' Both the Anthropic chat transformation and Bedrock Converse called ``AnthropicConfig._map_reasoning_effort`` and assigned the ``None`` it returns for ``"none"`` directly to ``optional_params["thinking"]``. Downstream ``is_thinking_enabled`` then did ``optional_params["thinking"].get("type")`` and crashed. Pop ``thinking`` (and on Claude 4.6/4.7, ``output_config``) instead of assigning ``None``, restoring the documented contract that ``reasoning_effort="none"`` means "do not enable thinking". This also prevents downstream Anthropic 400s ("thinking: Input should be an object", "output_config.effort: Input should be ...") if the bug were ever masked. Verified end-to-end against the live Anthropic API and Bedrock Converse on claude-opus-4-{5,6,7} and claude-sonnet-4-6, plus Bedrock Invoke for Claude 4.5/4.6. Vertex AI Anthropic and Azure AI Anthropic inherit the fixed ``map_openai_params`` from ``AnthropicConfig`` and need no further changes. * fix(vertex-ai): set response=null on batch error entries per OpenAI spec The Vertex batch output transformer was emitting both a populated 'response' and 'error' for failed batch entries. The OpenAI Batch output spec defines them as mutually exclusive: on error 'response' MUST be null. This broke any consumer using 'result["response"] is None' to detect failures. * test(vertex-ai): cover transformation_error path emits response=null * fix(security): sandbox jinja2 in gitlab/arize/bitbucket prompt managers DotpromptManager was hardened to render through ImmutableSandboxedEnvironment. The three sibling managers (gitlab, arize, bitbucket) were missed and still instantiate plain jinja2.Environment(), leaving the same attribute-traversal SSTI primitive open: a template fetched from a GitLab/BitBucket repo or Arize Phoenix workspace can reach __class__.__init__.__globals__ and execute arbitrary Python on the proxy host. Match the dotprompt pattern by switching all three to ImmutableSandboxedEnvironment. The sandbox blocks the dunder-traversal chain while leaving normal {{ var }} substitution intact, so the template surface is unchanged for legitimate use. Adds tests/test_litellm/integrations/test_prompt_manager_ssti.py (18 cases) verifying each manager's jinja_env is a sandbox, that classic SSTI payloads raise SecurityError, and that ordinary variable rendering still works. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(proxy): drop client-supplied pricing fields from request bodies The proxy currently forwards request-body pricing parameters (the fields on `CustomPricingLiteLLMParams`, plus `metadata.model_info`) into the core call path. Those fields belong to deployment configuration, not to per-request input — sending them from a client mutates the request's recorded cost and, via `litellm.completion` → `register_model`, the process-wide `litellm.model_cost` map for every later caller in the worker. Strip them at the boundary. The strip set is built from `CustomPricingLiteLLMParams.model_fields` so pricing fields added later are covered automatically. Operators who do want clients to supply per-request pricing can opt back in per key or team via `metadata.allow_client_pricing_override = true`, mirroring the existing `allow_client_mock_response` and `allow_client_message_redaction_opt_out` flags. Tests cover the strip set's coverage, root and metadata strips, the opt-in skip on both key and team metadata, and a regression check that the global `litellm.model_cost` map is unmutated after a stripped request. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(proxy): log stripped pricing fields at debug for operator visibility Operators upgrading would otherwise see client-supplied pricing overrides silently stop applying with no diagnostic. Emit a debug-level line listing the dropped fields and pointing at the opt-in flag when any are stripped; stay silent on the no-op path so the log isn't filled with noise. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(proxy): move pricing strip below the litellm_metadata JSON-string parse The strip ran before the proxy parses ``litellm_metadata`` from a JSON string into a dict (a path used by multipart/form-data and ``extra_body`` callers), so ``isinstance(metadata, dict)`` was False and ``model_info`` survived the strip. Move the call to the same post-parse position the ``user_api_key_*`` strip already uses for the same reason. Adds a regression test exercising the JSON-string ``litellm_metadata`` path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test(responses): replace legacy claude-4-sonnet alias in multiturn tool-call test Anthropic's main API no longer resolves the non-canonical 'claude-4-sonnet-20250514' alias for freshly issued keys, returning 404 not_found_error. PR #27031 already swept three other live tests pinned to this alias to claude-haiku-4-5-20251001 but missed test_multiturn_tool_calls in the responses API suite, which is now failing reliably on PR CI runs (e.g. PR #27074, job 1603363). Bump the two model references in test_multiturn_tool_calls to the same claude-haiku-4-5-20251001 snapshot used by PR #27031 -- it covers everything this test exercises (tool calling, multi-turn) and isn't on a deprecation schedule. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * chore(proxy): close callback-config and observability-credential side channels Two related gaps in the proxy's request bouncer: 1. ``is_request_body_safe`` (auth_utils.py) walked the request-body root and the ``litellm_embedding_config`` nested dict, but not ``metadata`` or ``litellm_metadata``. The same fields it bans at root — Langfuse / Langsmith / Arize / PostHog / Braintrust / Phoenix / W&B Weave / GCS / Humanloop / Lunary credentials and routing — were silently accepted when the caller put them inside metadata, retargeting observability callbacks to a caller-controlled host with caller-supplied creds. Walk both metadata containers (and parse the JSON-string form sent via multipart / ``extra_body``) through the same banned-params helper, so the existing ``allow_client_side_credentials`` opt-in covers both paths consistently. 2. The banned-params list was hand-maintained and lagged the canonical ``_supported_callback_params`` allow-list in ``initialize_dynamic_callback_params``. Derive the observability bans from that allow-list (minus a small ``_SAFE_CLIENT_CALLBACK_PARAMS`` set for informational fields like ``langfuse_prompt_version`` and ``langsmith_sampling_rate``) so future integrations are covered automatically; ``_EXTRA_BANNED_OBSERVABILITY_PARAMS`` carries the handful of fields integrations read but the allow-list hasn't caught up to. A guard test fails CI if a new entry is added to ``_supported_callback_params`` without an explicit safe-list decision. Separately in ``litellm_pre_call_utils.py``: add ``callbacks``, ``service_callback``, ``logger_fn``, and ``litellm_disabled_callbacks`` to ``_UNTRUSTED_ROOT_CONTROL_FIELDS``. The first three are appended to worker-wide ``litellm.{input,success,failure,_async_*,service}_callback`` lists / ``litellm.user_logger_fn`` from inside ``function_setup`` — one request poisons every subsequent caller in that worker. The last is the inverse primitive: the legitimate path reads it from key/team metadata, the request-body version silently disables admin-configured audit / observability for the call. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(auth): per-param allow must continue, not return early A pre-existing logic bug in ``_check_banned_params``: when the deployment-level ``configurable_clientside_auth_params`` permitted one banned field, the loop ``return``-ed on the first match instead of ``continue``-ing, so any other banned param later in the same body or metadata dict was never checked. This PR's metadata walk multiplies the surface where that bypass matters — a body pairing an allowed ``api_base`` with an observability credential like ``langfuse_host`` would silently pass. Proxy-wide ``allow_client_side_credentials`` keeps ``return`` (it's a global opt-in for every banned param). The per-param branch becomes ``continue`` so only the one explicitly-permitted field is skipped. Adds a regression test that exercises the api_base + langfuse_host pair. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(vector_store): resolve embedding config at request time, never persist creds The vector store create/update path previously called ``_resolve_embedding_config`` against the admin-configured router/DB model and persisted the resolved ``litellm_embedding_config`` dict (``api_key`` / ``api_base`` / ``api_version``) into the ``litellm_managedvectorstorestable.litellm_params`` column. Because the resolver expanded ``os.environ/...`` references via ``get_secret``, the DB row carried cleartext provider credentials, and the ``/vector_store/{new,info,update,list}`` responses returned them to any authenticated caller who could supply a known admin model name. Move the auto-resolve out of ``create_vector_store_in_db`` and out of the update path. Persist only the user-supplied ``litellm_embedding_model`` reference. Resolve at request-handling time inside ``_update_request_data_with_litellm_managed_vector_store_registry`` so the resolved config lives in the per-request ``data`` dict and is garbage-collected after the response. Legacy rows that were created by an earlier proxy version and already carry a resolved ``litellm_embedding_config`` skip the re-resolution and pass through unchanged so embedding calls keep working. The ``new_vector_store`` response now also runs the existing ``_redact_sensitive_litellm_params`` masker (already used by ``info``, ``update``, and ``list``), defending against caller-supplied cleartext on the create path and against legacy rows whose persisted credentials are still in the database. Existing tests that asserted the old write-time-resolve behaviour are updated to assert the new persistence shape (no embedding config stored, just the model reference). Two new tests cover the use-time path: one asserting fresh resolution happens when a row carries only the model reference, the other asserting legacy rows with persisted config skip re-resolution and continue to work. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(vector_store): tighten registry-mutation comment and dedupe test helpers * fix(vector_store): cache use-time embedding-config resolution Hold the resolved config in a process-memory TTL cache so the request-handling path doesn't run litellm_proxymodeltable.find_first on every vector-store call. * fix(anthropic,bedrock,vertex): forward output_config.effort + 400 on garbage reasoning_effort Follow-up bugs surfaced by the QA sweep on PR #27039 (https://github.com/BerriAI/litellm/pull/27039#issuecomment-4363363610). 1. Stop stripping output_config.effort on Bedrock + Vertex adaptive routes. - Vertex AI Claude 4.6/4.7 accepts output_config.effort on rawPredict (verified end-to-end against us-east5 / global). The strip helper now no-ops for effort. - Bedrock Converse routes output_config into additionalModelRequestFields for anthropic base models so the requested adaptive tier (low/medium/ high/xhigh/max) actually reaches the wire instead of all collapsing to identical thinking. - Bedrock Invoke chat transformation (AmazonAnthropicClaudeConfig) stops popping output_config from the post-AnthropicConfig request body. - Bedrock Invoke /v1/messages allowlist (BedrockInvokeAnthropicMessagesRequest) now lists output_config so the runtime allowlist filter forwards it. 2. Validate effort across Bedrock Converse so 'disabled' / 'invalid' / '' / unsupported tiers (xhigh/max on Sonnet 4.6 or budget-mode 4.5 models) surface as a clean 400 BadRequestError instead of 500. 3. ValueError -> BadRequestError throughout (AnthropicConfig.map_openai_params, _apply_output_config, AmazonConverseConfig._handle_reasoning_effort_parameter). Empty-string effort is now rejected (was silently passing the 'if effort and ...' short-circuit). 4. Floor reasoning_effort='minimal' at the Anthropic provider minimum (1024 budget_tokens) via new ANTHROPIC_MIN_THINKING_BUDGET_TOKENS so it's a usable tier on direct Anthropic / Azure AI Anthropic / Vertex AI Anthropic / Bedrock Invoke (all of which 400 below 1024). 5. model_prices: dedupe duplicate supports_max_reasoning_effort key on claude-opus-4-7 / claude-opus-4-7-20260416. Adds regression tests across all five affected paths; existing tests asserting the silent-strip behavior were updated to reflect the new pass-through and clean 400 surfaces. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(constants): make ANTHROPIC_MIN_THINKING_BUDGET_TOKENS a plain constant The documentation CI test (tests/documentation_tests/test_env_keys.py) asserts every os.getenv() key in the source has a matching entry in the litellm-docs config_settings.md table. ANTHROPIC_MIN_THINKING_BUDGET_TOKENS tracks Anthropic's published wire-protocol minimum (1024) — it's not a user-tunable, so making it env-overridable was wrong anyway. Drop the os.getenv() wrapper; the value is now a plain literal. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(anthropic,bedrock): correct effort error message and dedupe effort_map - Remove 'none' from the Bedrock _validate_anthropic_adaptive_effort error message; it was listed as a valid value but rejected by the membership check, leaving users in a feedback loop if they tried 'none'. - Hoist the duplicated reasoning_effort -> output_config.effort mapping out of AnthropicConfig.map_openai_params and AmazonConverseConfig._handle_reasoning_effort_parameter into a single AnthropicConfig.REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT class constant so the two routes cannot drift. * fix(anthropic): translate reasoning_effort on /v1/messages route Closes the remaining QA-sweep gap on PR #27074: Bedrock Invoke /v1/messages was silently ignoring ``reasoning_effort`` because the shared param filter only kept native Anthropic keys, so every effort tier collapsed to the same behavior on the wire (27/231 cells failing across opus-4-5 / opus-4-6 / sonnet-4-6). Map ``reasoning_effort`` to native Anthropic ``thinking`` / ``output_config.effort`` at the ``AnthropicMessagesConfig`` layer so all four /v1/messages routes (direct Anthropic, Azure AI, Vertex AI, Bedrock Invoke) inherit the same translation: - Add ``reasoning_effort`` to ``AnthropicMessagesRequestOptionalParams`` so the param filter in ``AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param`` no longer drops it before the transformation runs. - Add ``_translate_reasoning_effort_to_anthropic`` and call it from ``transform_anthropic_messages_request``. Mirrors ``AnthropicConfig.map_openai_params`` on the chat completion path (re-uses ``_map_reasoning_effort`` and ``REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT``) so the two routes cannot drift. Pops ``reasoning_effort`` so it never reaches the wire. - Caller-supplied native ``thinking`` / ``output_config.effort`` always win — same precedence as ``_translate_legacy_thinking_for_adaptive_model``. - Garbage values (``""``, ``"disabled"``, ``"invalid"``) raise ``AnthropicError(status_code=400)`` instead of falling through and surfacing as 500s from the provider. - ``"none"`` clears thinking + output_config so callers can opt out per request. Also restores the non-adaptive-model test coverage on Bedrock Invoke /v1/messages that the previous commit lost when ``test_bedrock_messages_strips_output_config`` was renamed to the ``forwards`` variant on Opus 4.7. Adds a new test file ``test_reasoning_effort_translation.py`` covering the translation at the shared config level (adaptive + non-adaptive models, none, garbage, caller precedence) so all four /v1/messages routes are exercised by a single suite. Adds parametrized + behavioral tests on the Bedrock Invoke /v1/messages suite covering: minimal/low/medium/high/xhigh/max mapping for adaptive models, thinking-budget mapping for non-adaptive Opus 4.5, ``none`` clears both, garbage raises 400, explicit ``output_config`` wins. Refs: https://github.com/BerriAI/litellm/pull/27074 * fix(anthropic,bedrock): reject unmapped reasoning_effort at mapping site Both the chat completion path (AnthropicConfig.map_openai_params) and the Bedrock Converse path (_handle_reasoning_effort_parameter) used REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(value, value) which falls back to the raw input on unmapped keys. Combined with _map_reasoning_effort returning type='adaptive' for any string on Claude 4.6/4.7, garbage values (e.g. 'disabled') could leak into optional_params['output_config']['effort'] unvalidated if map_openai_params ran without the downstream transform_request or _validate_anthropic_adaptive_effort check. Mirror the /v1/messages pattern: use .get(value) (no fallback) and raise BadRequestError immediately when the value is unmapped, co-locating validation with the mapping for defense in depth. * style: black formatting Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * fix(anthropic): stop class-attr leak; gate xhigh/max on every route The reasoning-effort mapping dict was a public class attribute on AnthropicConfig, so BaseConfig.get_config returned it as a request parameter and every Anthropic-backed call (Anthropic / Azure / Vertex / Bedrock Invoke) hit a 400 'REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT: Extra inputs are not permitted' from the provider. Move the mapping to a module-level constant. _supports_effort_level only looked the model up under custom_llm_provider='anthropic', so bedrock-prefixed model ids (e.g. bedrock/invoke/us.anthropic.claude-opus-4-7) returned False for both 'max' and 'xhigh' even when the underlying model entry has the flag set. Strip known provider prefixes and retry the lookup against litellm.model_cost directly so per-model gating works on every route. Mirror the per-model xhigh/max gate from AnthropicConfig._apply_output_config in AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic so the /v1/messages route also raises a clean 400 instead of forwarding the unsupported tier. * feat(anthropic,bedrock): strip output_config under drop_params for non-effort models When a proxy fronts Claude Code (which always sends `output_config.effort`) at a pre-4.5 Anthropic model — haiku-3, sonnet-3.5, opus-3, sonnet-4 — the forwarded knob causes a forced 400 the client can't fix. Gating a strip behind the existing `drop_params` flag lets operators opt into silent fixup once and stop worrying about per-model param hygiene. Default (`drop_params=False`) still forwards and surfaces the provider's error, preserving the strict, debuggable contract from #27074. Per https://platform.claude.com/docs/en/build-with-claude/effort the supporting set is Opus 4.5+, Sonnet 4.6+, and Mythos Preview; everything else is dropped (with a verbose_logger warning so the strip is visible). Recognition uses model-name patterns plus a fallback to any `supports_*_reasoning_effort` flag in the model map for forward compatibility with new entries. https://claude.ai/code/session_01WjHq31rvXT6xYNdVmSJvRp (cherry picked from commit1233943e78) * fix(base_llm): filter all _-prefixed class attrs from get_config The drop_params strip work added `AnthropicConfig._EFFORT_SUPPORTING_MODEL_PATTERNS` as a private class-level lookup tuple. `BaseConfig.get_config()` only filtered the `__`-prefixed names plus `_abc` / `_is_base_class`, so `_EFFORT_SUPPORTING_MODEL_PATTERNS` would have leaked into the request body the same way `REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT` did before the previous commit. Generalize the existing `_abc` / `_is_base_class` carve-outs to skip every `_`-prefixed name. `AmazonConverseConfig.get_config()` overrides the base method, so apply the same change there. Also unblocks future internal helpers from accidentally serialising into the wire body. * fix(anthropic): drive output_config.effort support from model map flags Replace hardcoded _EFFORT_SUPPORTING_MODEL_PATTERNS with a JSON-backed check that uses supports_*_reasoning_effort flags from the model map. Add supports_minimal_reasoning_effort: true to opus-4-5 and mythos-preview entries (which previously only carried supports_reasoning) so the JSON remains the single source of truth for effort capability. * fix(anthropic,bedrock,databricks): four reasoning_effort follow-ups - claude-sonnet-4-6 + reasoning_effort=max no longer 400s. Renamed _is_opus_4_6_model to _is_claude_4_6_model at three sites and added supports_max_reasoning_effort: true to 12 model entries in the JSON cost map (10 sonnet 4.6 ids + OpenRouter opus 4.6/4.7). - _map_reasoning_effort now raises BadRequestError(400) directly with llm_provider, instead of letting Databricks (and similar callers) surface its raw ValueError as a 500. - output_config.effort on Opus 4.5 over Bedrock no longer 400s for missing effort-2025-11-24 beta. Flipped JSON to "effort-2025-11-24" for bedrock + bedrock_converse and added an auto-attach branch in _process_tools_and_beta for non-adaptive Anthropic + output_config on Converse. - reasoning_effort=xhigh / =max on legacy budget-mode models (Haiku 4.5, Sonnet 4.5, Opus 4.5) now map to thinking.budget_tokens 8192 / 16384 instead of returning 400. Added two constants in litellm/constants.py. Tests updated for all four flips. Validated end-to-end via 306-cell live proxy matrix (6 model families x 3 routes x 17 effort cases), all pass. * fix(databricks): validate reasoning_effort and set output_config on adaptive Claude The Databricks path called `AnthropicConfig._map_reasoning_effort` for Claude models but never validated the effort string nor set `output_config.effort` for adaptive models (Claude 4.6/4.7). Since `_map_reasoning_effort` returns `type=adaptive` for ANY non-None / non-"none" string on adaptive models (including "disabled", "invalid", ""), Databricks silently accepted garbage and emitted a request without an `output_config.effort`, collapsing every adaptive tier to identical behavior. Match the Anthropic native, Bedrock Converse, Bedrock Invoke, and /v1/messages paths: when the resolved `thinking` is non-None on a 4.6/4.7 model, look up the value in `REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT` and either raise a clean `BadRequestError` or set `optional_params["output_config"]`. * fix(azure): omit model from image generation and image edit deployment requests Azure OpenAI routes image gen/edit by deployment in the URL; sending the deployment id in model breaks gpt-image-2 (invalid_value). Strip model from JSON for deployments/.../images/generations and from multipart data for .../images/edits. Non-deployment URLs (e.g. Azure AI FLUX) unchanged. Fixes #26316. Co-authored-by: Cursor <cursoragent@cursor.com> * test(azure): exercise image gen JSON filter via HTTP client; dedupe image edit URL - Image generation tests patch HTTPHandler.post / get_async_httpx_client so make_*_azure_httpx_request runs and wire json is asserted on call kwargs. - Azure image edit: strip model in finalize_image_edit_multipart_data using the same URL string the handler passes to POST (no second get_complete_url in transform). BaseImageEditConfig default finalize is a no-op. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(azure_ai/anthropic): promote output_config out of extra_body so validation runs `azure_ai` is registered in `litellm.openai_compatible_providers`, so `add_provider_specific_params_to_optional_params` (litellm/utils.py) auto-stuffs any non-OpenAI kwarg (e.g. `output_config={"effort": "..."}`) into `optional_params["extra_body"]`. `AzureAnthropicConfig.transform_request` then strips `extra_body` entirely on the way out, silently dropping the param — and `AnthropicConfig._apply_output_config` never sees it, so `effort="invalid"` / `effort="xhigh"` on a non-supporting model quietly reaches the model with default behavior instead of returning a clean 400 (as the native `anthropic` provider does). Promote the keys back to top-level `optional_params` (using `setdefault` so explicit top-level values win) before delegating to the parent `AnthropicConfig`. Apply in both `validate_environment` and `transform_request` so flag detection (`is_mcp_server_used`, etc.) and output-config validation both run. Surfaced by the QA matrix expansion on PR #27074: 20 cells where Azure returned 200 while `anthropic` returned 400 — all `output_config` mode across haiku_4_5, sonnet_4_5, opus_4_5, sonnet_4_6, opus_4_6, opus_4_7 families with `effort` in {invalid, xhigh, max, low, medium, high}. Tests: * `test_output_config_promoted_from_extra_body`: valid effort reaches data * `test_invalid_output_config_effort_raises_via_extra_body`: 400 on bad effort * `test_unsupported_effort_xhigh_raises_via_extra_body`: 400 on xhigh-on-Sonnet-4.6 * `test_extra_body_promotion_does_not_clobber_top_level`: setdefault semantics * test(image_gen): expect no model in Azure image edit multipart (#26316) Align test_azure_image_edit_litellm_sdk with deployment-scoped Azure edits. Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(anthropic): extract _validate_effort_for_model to prevent drift The chat completion path (`_apply_output_config`) and the /v1/messages pass-through (`AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic`) both gate `max` / `xhigh` per model. The two sites had diverged from near-identical copies into separately maintained blocks, creating a real drift risk when a new model tier (e.g. Claude 4.8) lands -- a contributor could update one site and miss the other. Centralise the gating in `AnthropicConfig._validate_effort_for_model`, which returns an error message string or `None`. Each call site keeps its own provider-appropriate exception type (`BadRequestError` for the chat path, `AnthropicError` for the /v1/messages pass-through) but the gating decision now comes from one place. Net -11 LOC. Adds a parametrised unit test exercising the helper directly across 4.5 / 4.6 / 4.7 model families and `max` / `xhigh` / lower-effort inputs. Existing tests at both call sites continue to pass unchanged. Addresses Greptile finding on PR #27074. * fix(databricks): narrow reasoning_effort_value to str for mypy `non_default_params.get("reasoning_effort")` returns `Any | None`, but `REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get()` expects `str`. Mypy flagged this on the strict pass. Narrow with `isinstance` before the lookup; non-strings fall through to the existing `BadRequestError` below with a clean validation message, so behavior is unchanged. Fixes a regression introduced by1a10746e95in this PR. * feat(proxy): add health_check_reasoning_effort for model health checks Co-authored-by: Cursor <cursoragent@cursor.com> * test(image_gen): align Azure image gen fixture with body omitting model Expected JSON matches deployment-scoped Azure POST (#26316). Co-authored-by: Cursor <cursoragent@cursor.com> * test(anthropic/chat): force PR-local model_cost map via autouse fixture CI runs without LITELLM_LOCAL_MODEL_COST_MAP=True, so litellm.model_cost is loaded from main-branch JSON (default model_cost_map_url) instead of the PR's checked-out model_prices_and_context_window.json. Tests that assert per-model flags added in this PR (supports_max_reasoning_effort, supports_xhigh_reasoning_effort) therefore pass locally but fail in CI with 'AssertionError: assert False is True' on 5 cases: - test_anthropic_model_supports_effort_param_recognizes_supporting_models [anthropic.claude-mythos-preview, bedrock/.../mythos-preview, claude-opus-4-5-20251101] - test_supports_effort_level_handles_provider_prefixes [bedrock/invoke/us.anthropic.claude-sonnet-4-6-max-True, claude-sonnet-4-6-max-True] Add an autouse fixture at tests/test_litellm/llms/anthropic/chat/conftest.py that monkey-patches litellm.model_cost to the PR-local JSON for every test in this directory. The parent conftest already snapshots+restores litellm.model_cost per-function, so the mutation is contained. This is a scoped workaround. The proper fix is to set the env var globally in the test workflow once the ~10 inline self-set test files are audited; tracking that as a follow-up issue. * [Fix] Docker: Pin Wolfi And Uv To Multi-Arch Index Digests The previous pins resolved to single-platform amd64 manifests, so buildx pulled the same amd64 base for both linux/amd64 and linux/arm64 targets. The published OCI index then advertised an arm64 entry whose layers are byte-identical to amd64 -- arm64 users got an amd64 binary. Switch all three Dockerfiles to the multi-arch image-index digests: - cgr.dev/chainguard/wolfi-base (index has linux/amd64 + linux/arm64) - ghcr.io/astral-sh/uv:0.11.7 (index has linux/amd64 + linux/arm64) Resolved with `docker buildx imagetools inspect <ref>` -- that returns the index digest. `docker pull` + `docker inspect` returns the per-host platform digest, which is what slipped in last time. * [Fix] Docker: Pin Uv To Multi-Arch Index Digest In Remaining Dockerfiles Apply the same fix to the three Dockerfiles not in the release pipeline today (alpine, dev, health_check) so they stay correct if/when they're built for arm64 in the future. Wolfi pins are not present in these files; the python:3.11-alpine and python:3.13-slim digests they already use are multi-arch indexes that include arm64/v8, so only the uv pin needed swapping. * fix(xai): fold reasoning_tokens into completion_tokens to satisfy OpenAI invariant xAI's chat completions API accounts reasoning_tokens separately from completion_tokens, but rolls them into total_tokens. This breaks the OpenAI invariant total_tokens == prompt_tokens + completion_tokens that downstream consumers (including litellm's own _usage_format_tests in tests/llm_translation/base_llm_unit_tests.py:58) rely on. Live capture (grok-3-mini-beta, 2026-05-04): prompt=14, completion=10, total=336, reasoning=312 14 + 10 = 24, NOT 336. OpenAI's o1/o3 reasoning models include reasoning_tokens in completion_tokens, leaving the prompt+completion=total invariant intact. xAI deviates. This patch aligns xAI to OpenAI semantics by folding reasoning_tokens into completion_tokens after the parent OpenAI parser runs. The fold is idempotent and defensive: - Only fires when total_tokens == prompt_tokens + completion_tokens + reasoning_tokens (the documented xAI shape). Refuses to fold if the gap doesn't match, guarding against silent corruption when xAI changes accounting. - Skips if completion_tokens already covers the gap (already normalised — e.g. cost calc replays a previously-folded Usage). xai.cost_calculator.cost_per_token already added reasoning_tokens to the visible completion count for billing. Post-fold the Usage block now satisfies that invariant directly, so the cost calc would double-bill. Updated cost_per_token to detect the OpenAI-normalised shape (total == prompt + completion) and skip the reasoning add-on in that case, falling through to the legacy raw-shape behaviour for callers that bypass the transformation (e.g. proxy log replay). Tests: - Adds TestXAIReasoningTokenFolding covering: gap-explained-fold, idempotent-no-double-fold, no-reasoning-skip, gap-mismatch-skip. - Adds test_already_normalised_usage_does_not_double_count_reasoning to lock the cost-calc idempotency. - Updates 7 pre-existing cost-calc tests whose total_tokens was internally inconsistent (used the OpenAI-normalised total but kept reasoning_tokens external) to use the documented xAI raw shape total = prompt + visible completion + reasoning. Pre-existing values masked the missing-fold by accident. Verified end-to-end against the live xAI API: LITELLM_LOCAL_MODEL_COST_MAP=False (CI default) + XAI_API_KEY set + pytest tests/llm_translation/test_xai.py::TestXAIChat::test_prompt_caching -> PASSED in 18.81s (was: AssertionError on usage.total_tokens == usage.prompt_tokens + usage.completion_tokens) 20/20 tests in tests/test_litellm/llms/xai/test_xai_cost_calculator.py and 8/8 in tests/test_litellm/llms/xai/test_xai_chat_transformation.py pass. * refactor(bedrock/converse): delegate effort gating to AnthropicConfig._validate_effort_for_model Removes the duplicated max/xhigh gating logic in _validate_anthropic_adaptive_effort and the now-unused _supports_effort_level_on_bedrock helper. Per-model gating now flows through the centralized AnthropicConfig._validate_effort_for_model (whose _supports_effort_level already strips Bedrock prefixes), so the chat completion, /v1/messages, and Bedrock Converse paths can't drift when a new gated effort tier is added. * Implement normalize_nonempty_secret_str function to trim whitespace from secrets and treat empty values as unset. Update proxy_server to use this function for Grafana credentials. Enhance tests to validate the new normalization behavior. * Fix qdrant semantic cache miss metadata * chore(deps): refresh dependency locks * chore(deps): authorize pytest license * fix: preserve tokenizer decode round trips * refactor(anthropic): drive adaptive-thinking gate via supports_adaptive_thinking flag Three of greptile's open comments on #27074 (P2 converse:512, P1 databricks:361, and the underlying capability-flag policy rule) flagged the same pattern: _is_claude_4_6_model(...) or _is_claude_4_7_model(...) used inline as a runtime 'is this an adaptive-thinking model?' check. That requires a code release each time a new adaptive Claude lands. Consolidate the inline gating to AnthropicModelInfo._is_adaptive_thinking_model, and switch the helper itself to read a new supports_adaptive_thinking flag from `model_prices_and_context_window.json` via `_supports_factory`, falling back to the family pattern only when the model-map entry doesn't carry the flag (preserves OpenRouter / Vercel / Bedrock-prefixed variants that route through the same code path with non-canonical ids). Adds `supports_adaptive_thinking: true` to the four 4.6/4.7 anthropic entries (opus-4-6 + dated, opus-4-7 + dated, sonnet-4-6). Bedrock-prefixed and Vertex-prefixed entries don't need the flag because both fall back through the family pattern (the helper short-circuits early on True from either path) and the bedrock/vertex Claude IDs all match the existing opus-4-{6,7} / sonnet-4-{6,7} pattern. Affected call sites: - `bedrock/chat/converse_transformation.py:_handle_reasoning_effort_parameter` - `anthropic/chat/transformation.py:_map_reasoning_effort` - `anthropic/chat/transformation.py:map_openai_params` (output_config branch) - `databricks/chat/transformation.py:map_openai_params` (output_config branch) The remaining `_is_claude_4_6_model` / `_is_claude_4_7_model` references in `AnthropicConfig._validate_effort_for_model` and `AnthropicConfig.get_supported_openai_params` are intentionally retained: they're per-model gating fallbacks for variants whose model-map entries don't yet carry the `supports_max_reasoning_effort` / `supports_reasoning` flag. Those are documented in-place. Tests: 537 anthropic/bedrock/databricks/vertex/messages tests pass. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * chore(deps): address dependency review notes * test(model_prices): add supports_adaptive_thinking to schema `test_aaamodel_prices_and_context_window_json_is_valid` validates the model-map JSON against an explicit schema with `additionalProperties`, so the new `supports_adaptive_thinking` flag added in98ced0ae43needs a matching schema entry. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * refactor: remove unnecessary comments from #27074 Strip out the explanatory and historical comments that don't carry business-logic justification. Comments that simply narrate what code does — or that explain prior behavior, what was changed, or which PR introduced a fix — are removed. Docstrings are reduced to a one-line summary where the long form repeated information already evident from the code or test data. No code-behavior changes. All 643 affected unit tests still pass. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test: keep decode token test local * chore(deps): align dashboard node engine * feat: selectively apply routing strategy according to model name * style: make _model_supports_effort_param more concise * refactor(anthropic,bedrock): hoist drop_params output_config warning to module constant Three call sites (anthropic chat, bedrock converse, bedrock invoke messages) emitted the same '...Effort is only supported on Opus 4.5+, Sonnet 4.6+, and Mythos Preview' warning verbatim. Extract DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING in litellm/llms/anthropic/chat/transformation.py and import it from the bedrock sites so future copy edits live in one place. Addresses Michael's review on PR #27074. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * refactor(anthropic,bedrock,databricks): factor BadRequestError for unknown reasoning_effort Three call sites raised the same BadRequestError("Invalid reasoning_effort: ... Must be one of 'minimal', 'low', ...") block when REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT returned None: anthropic chat map_openai_params, bedrock converse _handle_reasoning_effort_parameter, and databricks chat reasoning_effort path. Extract AnthropicConfig._raise_invalid_reasoning_effort(model, value, llm_provider) so future copy edits / valid-set changes happen in one place. Typed as NoReturn so type-checkers correctly narrow control flow at call sites. Addresses Michael's review on PR #27074. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * Clean up Redis semantic cache isolation fallback * fix(guardrails): align banned_keywords + azure_content_safety call_type gates with runtime route_type The hooks gated on ``call_type == "completion"`` but the proxy ingress passes ``route_type`` straight through as ``call_type`` — ``"acompletion"`` for /v1/chat/completions and ``"aresponses"`` for /v1/responses. Tests passed because they used the literal sync ``"completion"`` value, masking the gap. Switch both hooks to ``is_text_content_call_type`` (matches the canonical runtime values: completion / acompletion / aresponses) and update existing tests to assert against runtime values, plus parametrize a regression test that pins the gate. * fix: remove unused import * Add semantic cache legacy migration flag * Treat 0 team_member_budget as no cap * chore(caching): annotate qdrant quantization_params dict type Mypy infers the dict's value type from the first branch (Dict[str, bool]) which clashes with the scalar branch's mixed-type inner dict. Explicit Dict[str, Any] annotation lifts the inference. * chore(caching): remove allow_legacy_unscoped_cache_hits opt-in The flag was an opt-in escape hatch for the cross-tenant leak the rest of the patch closes — flipping it on (env var or constructor param) re-enables exactly the VERIA-54 primitive on either backend. There is no operational need that the secure path doesn't already meet: - Qdrant: legacy points without ``litellm_cache_key`` payload are excluded by the must-clause filter and treated as misses; new sets populate the cache key, so cold-start lasts only as long as the natural cache rebuild. - Redis: existing unscoped index can't carry the new schema; the init path falls back to ``{name}_isolated`` (and recreates it on stale schema), leaving the legacy index untouched. Drop the constructor param, env-var fallback, ``_using_legacy_unscoped_index`` flag, the legacy-reuse branch in ``_init_semantic_cache``, and the matching guards in set/get paths. Update tests to drop the legacy-mode cases and assert the secure-only behaviour. * fix(container): keep ownership-filter exceptions out of the LLM-error path filter_container_list_response runs after the upstream call has already succeeded; treating an ownership-lookup failure as an LLM-API error fires post_call_failure_hook for a successful upstream call and returns a misleading provider-shaped error to the client. Run the filter outside the try/except so genuine LLM errors stay scoped to the upstream call. * chore(container,skills): LRU eviction for owner caches; widen file_purpose Literal Two cleanups from the /simplify pass: * ``_CONTAINER_OWNER_CACHE`` and ``_SKILL_CACHE`` now LRU-evict via ``OrderedDict.popitem(last=False)`` instead of full ``clear()`` at capacity. Full clears converted a steady-state cached workload into a periodic full-DB-load oscillation as the cache repopulated from zero and cleared again. Reads now ``move_to_end`` so the just-touched entry survives the next eviction. Mirrors the pre-existing LRU pattern in ``_remember_container_owner``. * ``LiteLLM_ManagedObjectTable.file_purpose`` Literal now includes ``"container"`` so Pydantic validation accepts rows written by the ownership store. * chore(container,skills): drop legacy-access opt-out env vars LITELLM_ALLOW_UNTRACKED_CONTAINER_ACCESS and LITELLM_ALLOW_UNOWNED_SKILL_ACCESS were operator-toggleable opt-outs for the cross-tenant access primitive this PR closes — flipping either on re-enabled exactly the VERIA-20 read path. Default-secure with no escape hatch matches sibling fixes (vector-store cred isolation, semantic cache key isolation, user_config strip): all rejected the opt-out-of-security pattern. Untracked containers and unowned skills (rows that pre-date this enforcement) are admin-only. Non-admin owners need to either re-create via the now-tracked flow or have an admin assign ``created_by`` on the existing row. Update tests to assert the strict-only behaviour. * fix(ownership): reject identity-less callers instead of sharing a sentinel scope UNSCOPED_RESOURCE_OWNER_SCOPE collapsed every caller without an identity field (no user_id / team_id / org_id / api_key / token) into a single shared owner — a cross-tenant access primitive: any two such callers could see and delete each other's containers and skills. Drop the sentinel. ``get_primary_resource_owner_scope`` returns ``None`` and ``get_resource_owner_scopes`` returns ``[]`` for identity-less callers. ``record_container_owner`` and ``LiteLLMSkillsHandler.create_skill`` now reject creates from identity-less callers with a 403 instead of stamping the placeholder. Read paths already deny ``owner is None`` correctly so legacy rows (if any) are admin-only. * fix(proxy): include request-blocked callback params in auth bans * fix: keep skills handler FastAPI-free; fold gcs deny list into the body bouncer Two cleanups: * ``LiteLLMSkillsHandler.create_skill`` raised ``HTTPException`` for identity-less callers, importing FastAPI from a ``litellm/llms/`` module — that violates the project rule that FastAPI lives only under ``proxy/``. Switch to ``ValueError`` (the same shape the rest of the handler uses for not-found/forbidden) and update the test. * The proxy-auth body bouncer derived its observability ban list from ``_supported_callback_params`` only, missing ``_request_blocked_callback_params`` (where ``gcs_bucket_name`` and ``gcs_path_service_account`` live). Two recently-merged sibling PRs (#27019 added the deny list, #27081 added the test asserting these are rejected at the request body root) crossed without folding them together. Union the GCS deny list into the bouncer's derivation so the single source of truth covers both code paths. * fix(proxy): normalize managed resource team owner field * chore: simplify ownership tracking — drop thin stores, in-memory fallback, hand-rolled cache Substantial reduction (~765 LOC) without changing the security boundary: * Drop ContainerOwnershipStore and LiteLLMSkillsStore — both were one-method-per-Prisma-call wrappers. Inline the calls instead, matching the established pattern in vector_store_endpoints, agent_endpoints, and mcp_server/db.py. * Drop the prisma_client is None in-memory fallback. Production deploys always have Prisma; running ownership-critical paths on a process-local dict is a security footgun in the dev-mode case it was meant to support, and complicates every code path with a branch. Fail-secure: skip recording if Prisma is unavailable, and treat reads as "not found" (admin-only). * Drop the hand-rolled module-level cache. Replace with the existing litellm.caching.in_memory_cache.InMemoryCache, which already has TTL + max-size + eviction tested in its own module. Sentinel string for negative caching since InMemoryCache can't disambiguate "miss" from "cached as None". * Tests: drop coverage for removed code paths (in-memory fallback, hand-rolled cache internals). Keep tests for actual behavior (cache hit-rate, negative caching, owner check, list filtering, identity-less reject, admin bypass). * fix(container): cache list-allow-set, track admin-created containers Address Greptile P2 follow-ups from the prior round: * Cache ``_get_allowed_container_ids`` (60s LRU/TTL keyed by sorted owner-scope tuple) so ``GET /v1/containers`` doesn't issue a fresh ``find_many`` against ``litellm_managedobjecttable`` on every list call. Invalidate the caller's own cache entry when they record a new owner so the just-created container shows up on their next list. * Tighten the admin early-return in ``record_container_owner`` to skip ONLY when there's literally no container ID to stamp. An admin with identity (the master-key path populates ``user_id`` + ``api_key``) flows through the normal record path so admin-created containers are tracked like any other caller's. The truly-identity-less admin case still falls through to the 403 below — correct fail-secure default. Skill-cache invalidation gap (also flagged by Greptile) is moot: there is no skill update endpoint exposed; ownership-affecting mutations are only delete (already invalidates) and create (new ID, no cache entry to update). * chore(container): use delete_cache, json-encode scope key, clean test /simplify follow-ups: * Replace the two-``pop`` reach into ``cache_dict``/``ttl_dict`` with the existing public ``InMemoryCache.delete_cache(key)`` — the same idiom used elsewhere in the proxy. Bonus: ``delete_cache`` calls ``_remove_key`` which also handles ``expiration_heap`` consistency the direct pops were silently leaking. * JSON-encode the sorted scope list for the cache key instead of ``"|".join``. ``user_id`` / ``team_id`` / ``org_id`` / ``api_key`` are free-form strings and could contain a literal ``|`` — JSON quoting escapes any in-string separator unambiguously. * Extract ``_allowed_container_ids_cache_key()`` so the read and invalidation sites compute the key the same way. * Fix a placeholder-then-overwrite test construction: the ``__module__.split(".")[0] and "proxy_admin"`` line evaluated to a literal string that was immediately overwritten with the real enum value. Hoist the import and construct directly. * [Fix] Tests: Replace deprecated openrouter/claude-3.7-sonnet with claude-sonnet-4.5 OpenRouter has dropped active endpoints for anthropic/claude-3.7-sonnet, causing test_reasoning_content_completion to fail with a 404 "No endpoints found" error. Switch to anthropic/claude-sonnet-4.5, which is current and supports reasoning streaming. * feat: routing groups ui * fix(security): prevent secret_fields from leaking into spend logs secret_fields (containing raw HTTP headers including Authorization Bearer tokens) was being included in proxy_server_request['body'] because the body snapshot was a copy.copy(data) of the full request dict. This body gets serialized and persisted in the LiteLLM_SpendLogs table, exposing user credentials in the database. Root cause: data['secret_fields'] was set before the body snapshot at data['proxy_server_request']['body'] = copy.copy(data), so the full raw headers (including auth tokens) ended up in the snapshot. Fix (defense in depth): 1. Exclude 'secret_fields' when creating the body snapshot in litellm_pre_call_utils.py (primary fix) 2. Strip 'secret_fields' in _sanitize_request_body_for_spend_logs_payload as a secondary safeguard secret_fields remains available on the live data dict for legitimate downstream consumers (MCP, Responses API). Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com> * chore: update Next.js build artifacts (2026-05-05 02:13 UTC, node v20.20.2) * [Fix] Proxy: Break managed-resources import cycle on Python 3.13 The Python 3.13 CCI smoke matrix surfaces a partially-initialized-module ImportError when loading the managed files hook chain: litellm.proxy.hooks/__init__ (mid-import) -> enterprise.enterprise_hooks -> litellm_enterprise.proxy.hooks.managed_files -> litellm.llms.base_llm.managed_resources.isolation -> litellm.proxy.management_endpoints.common_utils -> litellm.proxy.utils (re-enters litellm.proxy.hooks) The except ImportError block in hooks/__init__.py silently swallowed the failure, leaving managed_files unregistered and POST /files returning 500 "Managed files hook not found". Two-layer fix: - Inline the 3-line _user_has_admin_view check in isolation.py instead of importing it from litellm.proxy.management_endpoints.common_utils. litellm.llms.* should not depend on litellm.proxy.* — removing this layering violation breaks the cycle at its root. - Define PROXY_HOOKS and get_proxy_hook before the conditional enterprise import in litellm/proxy/hooks/__init__.py, so any future re-entry resolves the public names instead of hitting an ImportError on a partially-initialized module. Also fold in two unrelated CCI repairs surfaced in the same staging run: - tests/otel_tests/test_key_logging_callbacks.py: per-key gcs_bucket_name / gcs_path_service_account are now stripped by initialize_dynamic_callback_params, so the GCS client falls through to the env-only branch. Update the assertion to match the new "GCS_BUCKET_NAME is not set" message. - .circleci/config.yml: tests/pass_through_tests now resolves google-auth-library@10.x via the @google-cloud/vertexai 1.12.0 bump, which uses dynamic ESM imports Jest 29 cannot load without --experimental-vm-modules. Pass that flag in the Vertex JS test step. Adds tests/test_litellm/proxy/hooks/test_proxy_hooks_init.py as a regression guard: managed_files / managed_vector_stores must register, and isolation.py must not transitively import litellm.proxy.utils. * [Fix] Proxy: Address Greptile feedback on hook-cycle PR - Move _user_has_admin_view to litellm.proxy._types as user_api_key_has_admin_view (single source of truth). common_utils.py and isolation.py both import from there now, removing the duplicated role-check that could silently diverge if new admin roles are added. - Add pytest.importorskip("litellm_enterprise") to the two regression tests that assert managed_files / managed_vector_stores are registered; those keys come from ENTERPRISE_PROXY_HOOKS so the tests would fail unconditionally in a checkout without the enterprise extra installed. * [Fix] Lint: Mark _user_has_admin_view re-export in common_utils Ruff F401 flagged the aliased import as unused within common_utils.py because the name is consumed only by external modules (~15 callers across guardrails, spend tracking, MCP, agents, management endpoints). Add `# noqa: F401 re-exported` so the alias survives lint while keeping a single source of truth in litellm.proxy._types. * refactor(azure): move image gen JSON helper; rename image edit finalize hook - Add image_generation/http_utils.azure_deployment_image_generation_json_body; call from azure.py (keeps AzureChatCompletion focused on chat). - Rename finalize_image_edit_multipart_data to finalize_image_edit_request_data with docstring covering multipart and JSON POST payloads (review feedback). Co-authored-by: Cursor <cursoragent@cursor.com> * test(proxy): cover health_check_reasoning_effort for completion mode Co-authored-by: Cursor <cursoragent@cursor.com> * [Fix] Tests: Use master key for /otel-spans in test_chat_completion_check_otel_spans /otel-spans now requires proxy admin (returns 401 'Only proxy admin can be used to generate, delete, update info for new keys/users/teams. Route=/otel-spans' for non-admin callers). Switch the GET call to use the master key sk-1234 while keeping the generated key for the chat-completion request that produces the spans. * [Fix] Tests: Pick chat-completion OTEL trace by content, not recency The /otel-spans endpoint returns process-wide spans and tags most_recent_parent by max start_time. After tightening that route to proxy_admin (sk-1234), the GET /otel-spans request itself emits auth spans that beat the chat-completion spans on start_time, so most_recent_parent now points at the request's own auth trace (['postgres', 'postgres']) and the >=5-span assertion fails. Pick the chat-completion trace by content: it is the only trace whose span list is a superset of {postgres, redis, raw_gen_ai_request, batch_write_to_db}. Verified locally end-to-end against otel_test_config.yaml + OTEL_EXPORTER=in_memory: 3/3 runs green. * [Fix] CI: Enable VCR replay for test_azure_o_series The Azure o-series tests were excluded from the conftest's VCR auto-marker because of a respx/vcrpy transport-patching conflict, but the only respx reference in the file was an unused `MockRouter` import. Drop the dead import and remove the file from the conflict set so cassettes record on first run and replay thereafter, eliminating the 60-95s live Azure latency that was crashing xdist workers under --timeout=120 thread-mode timeouts. * [Fix] Tests: Restore /metrics access for prometheus test suite /metrics now requires auth by default; tests/otel_tests/test_prometheus.py makes 4+ unauthenticated GETs against http://0.0.0.0:4000/metrics, so every prometheus test in CI now fails the metric assertion. Set require_auth_for_metrics_endpoint: false in otel_test_config.yaml to opt out for this test job, which scrapes /metrics directly. Verified locally: 8/8 prometheus tests green (one flaky retry on test_proxy_success_metrics that pre-dates this PR). Also drop the -x stop-on-first-failure flag from the otel test command so all failures in the job surface in a single CI run rather than hiding behind whichever one trips first. * [Perf] CI: Skip Redundant Playwright Apt Install in E2E UI Job The cimg/python:3.12-browsers base image already ships every Chromium system dependency Playwright needs (libnss3, libatk-bridge2.0-0, libcups2, etc. — the install log shows them all as "already the newest version"). Passing --with-deps to `npx playwright install` therefore runs an apt-get update + install for nothing, but pays the full cost of hitting Ubuntu mirrors. On a recent run those mirrors stalled hard: apt-get update alone took 6m53s at 81.5 kB/s with several archives returning connection refused. Drop --with-deps and persist ~/.cache/ms-playwright alongside node_modules so the Chromium binary is also reused across runs. Bump the cache key to v2 so the existing v1 entry (which only contained node_modules) is not loaded and skipped over the new browser path. * [Fix] Docker: Remove Hardcoded Prisma Binary Target For Multi-Arch Builds PRISMA_CLI_BINARY_TARGETS="debian-openssl-3.0.x" was hardcoded in docker/Dockerfile.non_root by #17695. On a buildx linux/arm64 leg this forces prisma to download the amd64 schema-engine into an arm64 image, so 'prisma migrate deploy' fails at startup with 'Could not find schema-engine binary'. Removing the env lets prisma auto-detect per build platform: amd64 builds still resolve to debian-openssl-3.0.x (Wolfi falls back to debian, same binary as before), and arm64 builds now correctly fetch linux-arm64-openssl-3.0.x. The offline-cache pre-warm goal of #17695 is preserved — only which binaries fill the cache changes. Fixes #19458 * [Fix] UI: Clear Admin Session Cookies Before Establishing Invited User's Session (#27227) The invite-signup form was writing the new user's token via raw `document.cookie` at `path=/`, while the rest of the auth surface uses `storeLoginToken` (which writes at `path=/ui` and mirrors to sessionStorage). After signup the inviter's `path=/ui` cookie kept winning path-specificity matching, and sessionStorage still held the inviter's token, so the dashboard rendered as the inviter rather than the newly created user. Treat invite signup as a principal-change boundary — clear prior session cookies first, then store the new token via the canonical helper. * test: add 24hr Redis-backed VCR cache to additional test suites (#27159) * test: add 24hr Redis-backed VCR cache to additional test suites Extracts the existing llm_translation VCR plumbing into a reusable helper (tests/_vcr_conftest_common.py) and wires it into the conftest.py files of the test directories listed in LIT-2787: audio_tests, batches_tests, guardrails_tests, image_gen_tests, litellm_utils_tests, local_testing, logging_callback_tests, pass_through_unit_tests, router_unit_tests, unified_google_tests The same helper is also adopted by the pre-existing llm_translation and llm_responses_api_testing conftests to remove the copy-pasted VCR setup. Each consuming conftest: - registers the Redis persister via pytest_recording_configure - auto-marks collected tests with pytest.mark.vcr (skipping respx-using files where applicable, since respx and vcrpy both patch httpx) - gates cassette writes on test success via _vcr_outcome_gate The cache is opt-in via CASSETTE_REDIS_URL; when unset, VCR is disabled and tests hit live providers as before. LITELLM_VCR_DISABLE=1 still forces a bypass for ad-hoc local runs. Test directories that run LiteLLM proxy in Docker (build_and_test, proxy_logging_guardrails_model_info_tests, proxy_store_model_in_db_tests) are intentionally not included: VCR.py patches the in-process httpx transport and cannot intercept calls made from inside a Docker container. The installing_litellm_on_python* jobs make no LLM calls and don't benefit from caching. https://linear.app/litellm-ai/issue/LIT-2787/add-24hr-caching-to-additional-test-suites * test(vcr): add safe-body matcher to handle JSONL and binary request bodies vcrpy's stock body matcher inspects Content-Type and unconditionally runs json.loads on application/json bodies. JSON Lines payloads (used by the Bedrock batch S3 PUT and other upload paths) crash that with json.JSONDecodeError: Extra data, before the matcher can return 'not a match'. This was the root cause of the batches_testing CI job failing on test_async_create_file once VCR auto-marking was applied to the batches_tests directory. Add a conservative byte-equality body matcher and use it in place of 'body' in the shared match_on tuple. The matcher is strictly more conservative than vcrpy's default — the only thing it gives up is 'different JSON key order is treated as the same body', which doesn't apply to deterministic litellm-built request payloads. It can never produce a false positive that the default would have rejected, so there is no cross-contamination risk. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): exclude tests that VCR replay actively breaks A few tests are incompatible with cassette replay and were failing on the latest CI run after VCR auto-marking was extended to local_testing and logging_callback_tests: - test_amazing_s3_logs.py (logging_callback_tests): the test asserts on a per-run response_id that should round-trip through a real S3 PUT/LIST. vcrpy's boto3 stub intercepts the PUT and the LIST replays stale keys, so the freshly-generated id is never found. - test_async_embedding_azure (logging_callback_tests) and test_amazing_sync_embedding (local_testing): the failure branches deliberately pass api_key='my-bad-key' to assert that the failure callback fires. We scrub auth headers from cassettes (so the bad-key request matches the prior good-key request), and vcrpy replays the recorded 200 — the failure callback never fires. - test_assistants.py (local_testing): the OpenAI Assistants polling APIs mint fresh thread/run IDs every recording session and then poll until status=='completed'. Replays of those polled GETs can never match a freshly-generated run id, so every CI run effectively re-records and the suite blows past the 15m no_output_timeout. Skip these from VCR auto-marking so they continue to hit live providers as they did before this change. The remaining tests in each directory still get cached. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): expand skip lists for second batch of incompatible tests Followup to the previous commit. After re-running CI on the rebuilt branch, three more tests surfaced as VCR-replay-incompatible: - litellm_utils_testing :: test_get_valid_models_from_dynamic_api_key Calls GET /v1/models with api_key='123' to assert the result is empty. We scrub auth headers, so the bad-key request matches the prior good-key cassette and replays the recorded model list. - litellm_utils_testing :: test_litellm_overhead.py Measures litellm_overhead_time_ms as a percentage of total wall-clock time. With cached responses the upstream 'network' time collapses to microseconds, blowing past the 40%% threshold the test asserts on. Skip the whole file (every parametrization is at risk). - local_testing_part1 :: test_async_custom_handler_completion and test_async_custom_handler_embedding Same bad-key failure-callback pattern as the already-skipped test_amazing_sync_embedding. - litellm_router_testing :: test_router_caching.py Asserts on litellm's own router-level response cache by comparing response1.id to response2.id across repeat upstream calls (test bypasses litellm cache via ttl=0 and expects upstream to return a *new* id). With VCR replay both upstream calls return the same cassette body, so the ids are identical. Skip the whole file. - logging_callback_tests :: test_async_chat_azure (preemptive) Same shape as already-skipped test_async_embedding_azure; was masked by upstream OpenAI rate-limit failures on baseline. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): use item.path and tighten matcher docstring - Replace pytest's deprecated item.fspath with item.path in apply_vcr_auto_marker_to_items so we don't emit deprecation warnings under pytest 8. - Clarify _safe_body_matcher docstring to reflect actual behavior (direct == first, then UTF-8 bytes comparison, no repr fallback). Addresses Greptile review feedback on PR #27159. * test(vcr): swallow all RedisError on cassette save/load Cassette persistence is strictly best-effort: any Redis-side failure (connection blip, timeout, OutOfMemoryError when the maxmemory cap is hit, READONLY replicas, etc.) should degrade to 'test passed but cassette not cached' rather than fail the test on teardown. Previously the persister only caught ConnectionError and TimeoutError, so OutOfMemoryError — which Redis Cloud raises when the cassette cache hits its memory cap and there are no evictable keys — propagated out of vcrpy's autouse fixture and ERRORed otherwise-passing tests on teardown. This caused the litellm_utils_testing CircleCI job to fail on the latest commit's run, even though the underlying test was a unit test that used mock_response and produced no real upstream traffic (the cassette was dirtied by a background langfuse callback). The rerun only succeeded because Redis evictions happened to free enough room before the SET — i.e. it was timing-dependent flakiness. Catch redis.exceptions.RedisError (the common base of all server- and client-side Redis exceptions) on both save and load, and parametrize the regression tests across ConnectionError, TimeoutError, and OutOfMemoryError to pin the new behavior. * test(vcr): surface cassette-cache failures with warnings + session banner When the persister silently swallows a Redis OOM (or any RedisError) on save/load there is otherwise no visible signal that the cache is degraded — tests pass, the cassette just isn't persisted, and the next session still hits the same Redis at the same near-cap memory. Add three layers of observability so that failure mode is loud: 1. Per-process health counters ("save_failures", "load_failures", and the last error string for each), exposed via cassette_cache_health() and reset via reset_cassette_cache_health(). The persister increments these in addition to logging. 2. VCRCassetteCacheWarning (UserWarning subclass) emitted via warnings.warn() inside the persister's except block. Pytest's built-in warnings summary at session end automatically lists every such warning, so the failure is visible in CI logs without any conftest-level wiring. 3. Session-end banner via emit_cassette_cache_session_banner() and a stderr-fallback atexit handler registered from register_persister_if_enabled(). Two states: - red "VCR CASSETTE CACHE DEGRADED" when save_failures or load_failures > 0 - yellow "VCR CASSETTE CACHE NEAR CAPACITY" (no failures, but used_memory >= 85% of maxmemory) so the next session knows the Redis is approaching OOM before any SET actually fails Capacity comes from a best-effort INFO memory probe (cassette_cache_capacity_snapshot) that returns None on any failure or when maxmemory is uncapped. The atexit handler skips xdist workers so only the controller emits. Tests: parametrize the existing save/load swallow-error tests across ConnectionError/TimeoutError/OutOfMemoryError, add direct tests for the health counters and warning emission, and a new test_vcr_conftest_common_banner.py covering banner output for every state (silent/red/yellow/disabled/xdist-worker). * test(vcr): bucket cassettes by API key fingerprint, drop bad-key skips Tests that deliberately call an LLM API with a bad key (e.g. to assert that the failure callback fires, or that check_valid_key returns False) were being silently served the prior good-key cassette: we scrub the real Authorization / x-api-key header from the cassette before storing it, so a follow-up bad-key call is byte-identical to the good-key call under the existing match_on tuple. Add a 'key_fingerprint' custom matcher that distinguishes requests by the SHA-256 of their API-key headers. The fingerprint is stamped into a synthetic 'x-litellm-key-fp' header by a new before_record_request hook, which then strips the real auth headers (we have to do the scrubbing here instead of via vcrpy's filter_headers knob, because filter_headers runs *first* and would erase the value we want to hash). Bad-key requests now get a different cassette bucket than good-key requests, so vcrpy will not replay a recorded 200 in place of the expected 401. The fingerprint is a one-way hash of the secret, so cassettes never contain the key. This permanently removes the 'bad-key' category of skips: - tests/local_testing: dropped ::test_amazing_sync_embedding, ::test_async_custom_handler_completion, ::test_async_custom_handler_embedding - tests/logging_callback_tests: dropped ::test_async_chat_azure, ::test_async_embedding_azure - tests/litellm_utils_tests: dropped ::test_get_valid_models_from_dynamic_api_key Coverage: 7 new unit tests in tests/test_litellm/test_vcr_safe_body_matcher.py covering header stripping, fingerprint determinism, no-auth bucketing, good-vs-bad key discrimination, x-api-key (Anthropic/Azure) discrimination, and idempotence under replay. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): drop redundant comments and docstrings Trim narration of code that is already self-evident from function and variable names. Keep the two genuinely non-obvious bits: - ordering constraint between filter_headers and before_record_request, which would invite a maintainer to re-introduce the bug if removed - the per-directory _VCR_INCOMPATIBLE_FILES rationale, since 'why exactly is this skipped' is not knowable from the test name alone Also drop the 40-line commented-out drop-in conftest snippet at the bottom of _vcr_conftest_common.py — the consuming conftests are the canonical reference. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): make _before_record_request idempotent vcrpy invokes before_record_request more than once per request: can_play_response_for calls it, then __contains__ / _responses (reached via play_response) call it again on the result. The second invocation sees a request whose auth headers we already stripped, so a naive recompute yields "no-key" and overwrites the real fingerprint stored in the header. This makes can_play_response_for and play_response disagree on matchability — the former says "yes, we have a stored response for this" (matching no-key to no-key) and the latter throws UnhandledHTTPRequestError because it computes a fresh real fingerprint that doesn't match the stored no-key. In CI this manifested as ~30 failing tests across guardrails_testing, audio_testing, batches_testing, image_gen_testing, llm_responses_api, litellm_router_unit_testing, etc. Skip the recompute when the header is already set, so re-applying the hook is a no-op. Adds a regression test that fires the hook twice on the same dict and asserts the fingerprint stays put. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): drop more redundant docstrings and headers * test(vcr): enable 24hr cache for ocr_tests and search_tests These two directories were the only non-dockerized test suites in the build_and_test workflow that make live LLM/provider API calls but were not VCR-enabled by this PR. Together they account for 96 tests: - tests/ocr_tests/ (31): Mistral OCR, Azure AI OCR, Azure Document Intelligence, Vertex AI OCR. Pure-unit tests inside the same files (e.g. TestAzureDocumentIntelligencePagesParam) make no HTTP calls and become benign VCR NOOPs. - tests/search_tests/ (65): Brave, DataForSEO, DuckDuckGo, Exa, Firecrawl, Google PSE, Linkup, Parallel.ai, Perplexity, SearchAPI, Searxng, Serper, Tavily. Both directories use the canonical minimal conftest pattern from tests/audio_tests/conftest.py with no skip lists. None of the test files use respx, none assert on per-call upstream non-determinism (no response1.id != response2.id, no overhead-as-fraction-of-total, no live polling), so the default match_on tuple should cache cleanly. If a flake surfaces during the first cassette-recording CI run, we can add a targeted skip the same way we did for the other dirs. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * [Fix] Team UI: handle legacy dict shape for metadata.guardrails (#27224) * [Fix] Team UI: handle legacy dict shape for metadata.guardrails A team can have metadata.guardrails stored as {"modify_guardrails": bool} (the permission-flag shape introduced in PR #4810) rather than the expected string[]. The opt-out logic added in PR #25575 calls .filter() on this field, which throws TypeError on a dict and crashes the team detail page. Add a safeGuardrailsList helper that returns [] when the field is not an array, and route the three read sites through it. * [Fix] Team UI: inline Array.isArray guards for guardrails metadata Replace the safeGuardrailsList helper with inline Array.isArray checks at each call site, and apply the same guard to opted_out_global_guardrails for consistency. No known legacy dict rows for opted_out_global_guardrails, but the unguarded `|| []` pattern is the same shape risk. Six call sites now defended directly: three for metadata.guardrails and three for metadata.opted_out_global_guardrails. * chore: update Next.js build artifacts (2026-05-05 22:45 UTC, node v20.20.2) (#27240) * [Infra] Bump deps (#27157) * bump: version 0.4.70 → 0.4.71 * bump: version 0.1.39 → 0.1.40 * uv lock --------- Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain> Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: user <70670632+stuxf@users.noreply.github.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: shivam <shivam@berri.ai> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> Co-authored-by: Krrish Dholakia <krrish+github@berri.ai> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: Sameer Kankute <sameer@berri.ai> Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com> Co-authored-by: Michael-RZ-Berri <michael@berri.ai> Co-authored-by: harish-berri <harish@berri.ai> Co-authored-by: Yassin Kortam <yassinkortam@g.ucla.edu> Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local> Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com>
39972 lines
1.4 MiB
39972 lines
1.4 MiB
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"notes": "Flux Dev - Development version optimized for experimentation"
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"aiml/flux/schnell": {
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"litellm_provider": "aiml",
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"metadata": {
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"notes": "Flux Schnell - Fast generation model optimized for speed"
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},
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"mode": "image_generation",
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"source": "https://docs.aimlapi.com/",
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"supported_endpoints": [
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"/v1/images/generations"
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256000.0,
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1000000.0
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32000.0
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128000.0
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128000.0,
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252000.0
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32000.0
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32000.0,
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128000.0
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128000.0,
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252000.0
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0,
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32000.0
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32000.0,
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128000.0
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{
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128000.0,
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252000.0
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}
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]
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},
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"dashscope/qwen3-next-80b-a3b-instruct": {
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"input_cost_per_token": 1.5e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 262144,
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"max_output_tokens": 65536,
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"max_tokens": 65536,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"dashscope/qwen3-next-80b-a3b-thinking": {
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"input_cost_per_token": 1.5e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 262144,
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"max_output_tokens": 65536,
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"max_tokens": 65536,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
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"input_cost_per_token": 4e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 131072,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"dashscope/qwen3-vl-235b-a22b-thinking": {
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"input_cost_per_token": 4e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 131072,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"dashscope/qwen3-vl-32b-instruct": {
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"litellm_provider": "dashscope",
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"max_input_tokens": 131072,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"dashscope/qwen3-vl-32b-thinking": {
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"input_cost_per_token": 1.6e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 131072,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing",
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"dashscope/qwen3-vl-plus": {
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"litellm_provider": "dashscope",
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"max_input_tokens": 260096,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/models",
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{
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0,
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32000.0
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]
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},
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{
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"output_cost_per_token": 2.4e-06,
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32000.0,
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128000.0
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]
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"range": [
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128000.0,
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256000.0
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]
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}
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]
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},
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"dashscope/qwen3.5-plus": {
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"max_input_tokens": 991808,
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"max_output_tokens": 65536,
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"mode": "chat",
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"source": "https://www.alibabacloud.com/help/en/model-studio/models",
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"supports_reasoning": true,
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"supports_tool_choice": true,
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"supports_vision": true,
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{
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"output_cost_per_token": 2.4e-06,
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"range": [
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0,
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256000.0
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]
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},
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{
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"output_cost_per_token": 3e-06,
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"range": [
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256000.0,
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1000000.0
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]
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}
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]
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},
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"dashscope/qwq-plus": {
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"input_cost_per_token": 8e-07,
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"litellm_provider": "dashscope",
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"max_input_tokens": 98304,
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"max_output_tokens": 8192,
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"max_tokens": 8192,
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"mode": "chat",
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"output_cost_per_token": 2.4e-06,
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"source": "https://www.alibabacloud.com/help/en/model-studio/models",
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
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"dashscope/qwen-image-2.0": {
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"litellm_provider": "dashscope",
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"mode": "image_generation",
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"source": "https://www.alibabacloud.com/help/en/model-studio/models",
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"supported_endpoints": [
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"/v1/images/generations"
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]
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},
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"dashscope/qwen-image-2.0-pro": {
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"litellm_provider": "dashscope",
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"mode": "image_generation",
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"source": "https://www.alibabacloud.com/help/en/model-studio/models",
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"supported_endpoints": [
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"/v1/images/generations"
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]
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},
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"databricks/databricks-bge-large-en": {
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"input_cost_per_token": 1.0003e-07,
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"input_dbu_cost_per_token": 1.429e-06,
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"litellm_provider": "databricks",
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"max_input_tokens": 512,
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"max_tokens": 512,
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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},
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"mode": "embedding",
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"output_cost_per_token": 0.0,
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"output_dbu_cost_per_token": 0.0,
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"output_vector_size": 1024,
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"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
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},
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"databricks/databricks-claude-3-7-sonnet": {
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"input_cost_per_token": 2.9999900000000002e-06,
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"input_dbu_cost_per_token": 4.2857e-05,
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
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"max_output_tokens": 128000,
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"max_tokens": 128000,
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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},
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"mode": "chat",
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"output_cost_per_token": 1.5000020000000002e-05,
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"output_dbu_cost_per_token": 0.000214286,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
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"databricks/databricks-claude-haiku-4-5": {
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"input_cost_per_token": 1.00002e-06,
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"input_dbu_cost_per_token": 1.4286e-05,
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
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"max_output_tokens": 64000,
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"max_tokens": 64000,
|
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
|
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"mode": "chat",
|
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"output_cost_per_token": 5.00003e-06,
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"output_dbu_cost_per_token": 7.1429e-05,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
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"databricks/databricks-claude-opus-4": {
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"input_cost_per_token": 1.5000020000000002e-05,
|
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"input_dbu_cost_per_token": 0.000214286,
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
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"max_output_tokens": 32000,
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"max_tokens": 32000,
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
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"mode": "chat",
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"output_cost_per_token": 7.500003000000001e-05,
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"output_dbu_cost_per_token": 0.001071429,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
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"databricks/databricks-claude-opus-4-1": {
|
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"input_cost_per_token": 1.5000020000000002e-05,
|
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"input_dbu_cost_per_token": 0.000214286,
|
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
|
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"max_output_tokens": 32000,
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"max_tokens": 32000,
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
|
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"mode": "chat",
|
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"output_cost_per_token": 7.500003000000001e-05,
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"output_dbu_cost_per_token": 0.001071429,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
|
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},
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"databricks/databricks-claude-opus-4-5": {
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"input_cost_per_token": 5.00003e-06,
|
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"input_dbu_cost_per_token": 7.1429e-05,
|
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
|
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"max_output_tokens": 64000,
|
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"max_tokens": 64000,
|
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
|
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"mode": "chat",
|
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"output_cost_per_token": 2.5000010000000002e-05,
|
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"output_dbu_cost_per_token": 0.000357143,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
|
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"supports_minimal_reasoning_effort": true,
|
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"supports_tool_choice": true
|
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},
|
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"databricks/databricks-claude-sonnet-4": {
|
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"input_cost_per_token": 2.9999900000000002e-06,
|
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"input_dbu_cost_per_token": 4.2857e-05,
|
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
|
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"max_output_tokens": 64000,
|
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"max_tokens": 64000,
|
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"metadata": {
|
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
|
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"mode": "chat",
|
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"output_cost_per_token": 1.5000020000000002e-05,
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"output_dbu_cost_per_token": 0.000214286,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
|
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},
|
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"databricks/databricks-claude-sonnet-4-1": {
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"input_cost_per_token": 2.9999900000000002e-06,
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"input_dbu_cost_per_token": 4.2857e-05,
|
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"litellm_provider": "databricks",
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"max_input_tokens": 200000,
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"max_output_tokens": 64000,
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"max_tokens": 64000,
|
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"metadata": {
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"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
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},
|
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"mode": "chat",
|
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"output_cost_per_token": 1.5000020000000002e-05,
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"output_dbu_cost_per_token": 0.000214286,
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"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_reasoning": true,
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"supports_tool_choice": true
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},
|
|
"databricks/databricks-claude-sonnet-4-5": {
|
|
"input_cost_per_token": 2.9999900000000002e-06,
|
|
"input_dbu_cost_per_token": 4.2857e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 200000,
|
|
"max_output_tokens": 64000,
|
|
"max_tokens": 64000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5000020000000002e-05,
|
|
"output_dbu_cost_per_token": 0.000214286,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
|
|
"supports_assistant_prefill": true,
|
|
"supports_function_calling": true,
|
|
"supports_reasoning": true,
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-gemini-2-5-flash": {
|
|
"input_cost_per_token": 3.0001999999999996e-07,
|
|
"input_dbu_cost_per_token": 4.285999999999999e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 1048576,
|
|
"max_output_tokens": 65535,
|
|
"max_tokens": 65535,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 2.49998e-06,
|
|
"output_dbu_cost_per_token": 3.5714e-05,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
|
|
"supports_function_calling": true,
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-gemini-2-5-pro": {
|
|
"input_cost_per_token": 1.24999e-06,
|
|
"input_dbu_cost_per_token": 1.7857e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 1048576,
|
|
"max_output_tokens": 65536,
|
|
"max_tokens": 65536,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 9.999990000000002e-06,
|
|
"output_dbu_cost_per_token": 0.000142857,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
|
|
"supports_function_calling": true,
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-gemma-3-12b": {
|
|
"input_cost_per_token": 1.5000999999999998e-07,
|
|
"input_dbu_cost_per_token": 2.1429999999999996e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 128000,
|
|
"max_output_tokens": 32000,
|
|
"max_tokens": 32000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 5.0001e-07,
|
|
"output_dbu_cost_per_token": 7.143e-06,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-5": {
|
|
"input_cost_per_token": 1.24999e-06,
|
|
"input_dbu_cost_per_token": 1.7857e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 272000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 9.999990000000002e-06,
|
|
"output_dbu_cost_per_token": 0.000142857,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-5-1": {
|
|
"input_cost_per_token": 1.24999e-06,
|
|
"input_dbu_cost_per_token": 1.7857e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 272000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 9.999990000000002e-06,
|
|
"output_dbu_cost_per_token": 0.000142857,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-5-mini": {
|
|
"input_cost_per_token": 2.4997000000000006e-07,
|
|
"input_dbu_cost_per_token": 3.571e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 272000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.9999700000000004e-06,
|
|
"output_dbu_cost_per_token": 2.8571e-05,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-5-nano": {
|
|
"input_cost_per_token": 4.998e-08,
|
|
"input_dbu_cost_per_token": 7.14e-07,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 272000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 3.9998000000000007e-07,
|
|
"output_dbu_cost_per_token": 5.714000000000001e-06,
|
|
"source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-oss-120b": {
|
|
"input_cost_per_token": 1.5000999999999998e-07,
|
|
"input_dbu_cost_per_token": 2.1429999999999996e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 131072,
|
|
"max_output_tokens": 131072,
|
|
"max_tokens": 131072,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 5.9997e-07,
|
|
"output_dbu_cost_per_token": 8.571e-06,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gpt-oss-20b": {
|
|
"input_cost_per_token": 7e-08,
|
|
"input_dbu_cost_per_token": 1e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 131072,
|
|
"max_output_tokens": 131072,
|
|
"max_tokens": 131072,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 3.0001999999999996e-07,
|
|
"output_dbu_cost_per_token": 4.285999999999999e-06,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
|
|
},
|
|
"databricks/databricks-gte-large-en": {
|
|
"input_cost_per_token": 1.2999000000000001e-07,
|
|
"input_dbu_cost_per_token": 1.857e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 8192,
|
|
"max_tokens": 8192,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "embedding",
|
|
"output_cost_per_token": 0.0,
|
|
"output_dbu_cost_per_token": 0.0,
|
|
"output_vector_size": 1024,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
|
|
},
|
|
"databricks/databricks-llama-2-70b-chat": {
|
|
"input_cost_per_token": 5.0001e-07,
|
|
"input_dbu_cost_per_token": 7.143e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 4096,
|
|
"max_output_tokens": 4096,
|
|
"max_tokens": 4096,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5000300000000002e-06,
|
|
"output_dbu_cost_per_token": 2.1429e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-llama-4-maverick": {
|
|
"input_cost_per_token": 5.0001e-07,
|
|
"input_dbu_cost_per_token": 7.143e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 128000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5000300000000002e-06,
|
|
"output_dbu_cost_per_token": 2.1429e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-meta-llama-3-1-405b-instruct": {
|
|
"input_cost_per_token": 5.00003e-06,
|
|
"input_dbu_cost_per_token": 7.1429e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 128000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5000020000000002e-05,
|
|
"output_dbu_cost_per_token": 0.000214286,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-meta-llama-3-1-8b-instruct": {
|
|
"input_cost_per_token": 1.5000999999999998e-07,
|
|
"input_dbu_cost_per_token": 2.1429999999999996e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 200000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 4.5003000000000007e-07,
|
|
"output_dbu_cost_per_token": 6.429000000000001e-06,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving"
|
|
},
|
|
"databricks/databricks-meta-llama-3-3-70b-instruct": {
|
|
"input_cost_per_token": 5.0001e-07,
|
|
"input_dbu_cost_per_token": 7.143e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 128000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5000300000000002e-06,
|
|
"output_dbu_cost_per_token": 2.1429e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-meta-llama-3-70b-instruct": {
|
|
"input_cost_per_token": 1.00002e-06,
|
|
"input_dbu_cost_per_token": 1.4286e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 128000,
|
|
"max_output_tokens": 128000,
|
|
"max_tokens": 128000,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 2.9999900000000002e-06,
|
|
"output_dbu_cost_per_token": 4.2857e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-mixtral-8x7b-instruct": {
|
|
"input_cost_per_token": 5.0001e-07,
|
|
"input_dbu_cost_per_token": 7.143e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 4096,
|
|
"max_output_tokens": 4096,
|
|
"max_tokens": 4096,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.00002e-06,
|
|
"output_dbu_cost_per_token": 1.4286e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-mpt-30b-instruct": {
|
|
"input_cost_per_token": 1.00002e-06,
|
|
"input_dbu_cost_per_token": 1.4286e-05,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 8192,
|
|
"max_output_tokens": 8192,
|
|
"max_tokens": 8192,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.00002e-06,
|
|
"output_dbu_cost_per_token": 1.4286e-05,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"databricks/databricks-mpt-7b-instruct": {
|
|
"input_cost_per_token": 5.0001e-07,
|
|
"input_dbu_cost_per_token": 7.143e-06,
|
|
"litellm_provider": "databricks",
|
|
"max_input_tokens": 8192,
|
|
"max_output_tokens": 8192,
|
|
"max_tokens": 8192,
|
|
"metadata": {
|
|
"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 0.0,
|
|
"output_dbu_cost_per_token": 0.0,
|
|
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
|
"supports_tool_choice": true
|
|
},
|
|
"dataforseo/search": {
|
|
"input_cost_per_query": 0.003,
|
|
"litellm_provider": "dataforseo",
|
|
"mode": "search"
|
|
},
|
|
"davinci-002": {
|
|
"input_cost_per_token": 2e-06,
|
|
"litellm_provider": "text-completion-openai",
|
|
"max_input_tokens": 16384,
|
|
"max_output_tokens": 4096,
|
|
"max_tokens": 4096,
|
|
"mode": "completion",
|
|
"output_cost_per_token": 2e-06
|
|
},
|
|
"deepgram/base": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/base-conversationalai": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/base-finance": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/base-general": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
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},
|
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"deepgram/base-meeting": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
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"deepgram/base-phonecall": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/base-video": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/base-voicemail": {
|
|
"input_cost_per_second": 0.00020833,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0125/60 seconds = $0.00020833 per second",
|
|
"original_pricing_per_minute": 0.0125
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/enhanced": {
|
|
"input_cost_per_second": 0.00024167,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0145/60 seconds = $0.00024167 per second",
|
|
"original_pricing_per_minute": 0.0145
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/enhanced-finance": {
|
|
"input_cost_per_second": 0.00024167,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0145/60 seconds = $0.00024167 per second",
|
|
"original_pricing_per_minute": 0.0145
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
|
"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/enhanced-general": {
|
|
"input_cost_per_second": 0.00024167,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0145/60 seconds = $0.00024167 per second",
|
|
"original_pricing_per_minute": 0.0145
|
|
},
|
|
"mode": "audio_transcription",
|
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"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/enhanced-meeting": {
|
|
"input_cost_per_second": 0.00024167,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0145/60 seconds = $0.00024167 per second",
|
|
"original_pricing_per_minute": 0.0145
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/enhanced-phonecall": {
|
|
"input_cost_per_second": 0.00024167,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0145/60 seconds = $0.00024167 per second",
|
|
"original_pricing_per_minute": 0.0145
|
|
},
|
|
"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/nova": {
|
|
"input_cost_per_second": 7.167e-05,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
|
"original_pricing_per_minute": 0.0043
|
|
},
|
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"mode": "audio_transcription",
|
|
"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/nova-2": {
|
|
"input_cost_per_second": 7.167e-05,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
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"original_pricing_per_minute": 0.0043
|
|
},
|
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"mode": "audio_transcription",
|
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"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
|
"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/nova-2-atc": {
|
|
"input_cost_per_second": 7.167e-05,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
|
"original_pricing_per_minute": 0.0043
|
|
},
|
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"mode": "audio_transcription",
|
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"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
|
|
"deepgram/nova-2-automotive": {
|
|
"input_cost_per_second": 7.167e-05,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
|
"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
|
"original_pricing_per_minute": 0.0043
|
|
},
|
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"mode": "audio_transcription",
|
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"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
|
|
"supported_endpoints": [
|
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"/v1/audio/transcriptions"
|
|
]
|
|
},
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"deepgram/nova-2-conversationalai": {
|
|
"input_cost_per_second": 7.167e-05,
|
|
"litellm_provider": "deepgram",
|
|
"metadata": {
|
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
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"original_pricing_per_minute": 0.0043
|
|
},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
|
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"supported_endpoints": [
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"/v1/audio/transcriptions"
|
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]
|
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},
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|
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"litellm_provider": "deepgram",
|
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
|
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},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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|
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"litellm_provider": "deepgram",
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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|
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},
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
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"metadata": {
|
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
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"original_pricing_per_minute": 0.0043
|
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},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
|
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},
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"litellm_provider": "deepgram",
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"metadata": {
|
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
|
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"original_pricing_per_minute": 0.0043
|
|
},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
|
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
|
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]
|
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},
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|
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"litellm_provider": "deepgram",
|
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"metadata": {
|
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"calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)",
|
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"original_pricing_per_minute": 0.0052
|
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},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
|
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},
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"metadata": {
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"calculation": "$0.0043/60 seconds = $0.00007167 per second",
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"original_pricing_per_minute": 0.0043
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},
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"deepgram/whisper": {
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"input_cost_per_second": 0.0001,
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"litellm_provider": "deepgram",
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"metadata": {
|
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
|
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},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"litellm_provider": "deepgram",
|
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"metadata": {
|
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
|
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},
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"mode": "audio_transcription",
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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|
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"litellm_provider": "deepgram",
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"metadata": {
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
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"source": "https://deepgram.com/pricing",
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]
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"litellm_provider": "deepgram",
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"metadata": {
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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"metadata": {
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
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},
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"deepgram/whisper-tiny": {
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"input_cost_per_second": 0.0001,
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"litellm_provider": "deepgram",
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"metadata": {
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"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
|
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},
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"mode": "audio_transcription",
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"output_cost_per_second": 0.0,
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"source": "https://deepgram.com/pricing",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"max_tokens": 4096,
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"max_input_tokens": 4096,
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"max_output_tokens": 4096,
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"input_cost_per_token": 8e-08,
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"output_cost_per_token": 9e-08,
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"litellm_provider": "deepinfra",
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"mode": "chat",
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"supports_tool_choice": true,
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"supports_function_calling": true
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},
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"mode": "chat",
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"supports_tool_choice": true,
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"notes": "Pricing listed as $0.75/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 3.75e-07,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
|
"nscale/deepseek-ai/DeepSeek-R1-Distill-Llama-8B": {
|
|
"input_cost_per_token": 2.5e-08,
|
|
"litellm_provider": "nscale",
|
|
"metadata": {
|
|
"notes": "Pricing listed as $0.05/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 2.5e-08,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
|
"nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B": {
|
|
"input_cost_per_token": 9e-08,
|
|
"litellm_provider": "nscale",
|
|
"metadata": {
|
|
"notes": "Pricing listed as $0.18/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 9e-08,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
|
"nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": {
|
|
"input_cost_per_token": 7e-08,
|
|
"litellm_provider": "nscale",
|
|
"metadata": {
|
|
"notes": "Pricing listed as $0.14/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 7e-08,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
|
"nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B": {
|
|
"input_cost_per_token": 1.5e-07,
|
|
"litellm_provider": "nscale",
|
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"metadata": {
|
|
"notes": "Pricing listed as $0.30/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 1.5e-07,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
|
"nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B": {
|
|
"input_cost_per_token": 2e-07,
|
|
"litellm_provider": "nscale",
|
|
"metadata": {
|
|
"notes": "Pricing listed as $0.40/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 2e-07,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
|
},
|
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"nscale/meta-llama/Llama-3.1-8B-Instruct": {
|
|
"input_cost_per_token": 3e-08,
|
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"litellm_provider": "nscale",
|
|
"metadata": {
|
|
"notes": "Pricing listed as $0.06/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
|
"output_cost_per_token": 3e-08,
|
|
"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
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},
|
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"nscale/meta-llama/Llama-3.3-70B-Instruct": {
|
|
"input_cost_per_token": 2e-07,
|
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"litellm_provider": "nscale",
|
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"metadata": {
|
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"notes": "Pricing listed as $0.40/1M tokens total. Assumed 50/50 split for input/output."
|
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},
|
|
"mode": "chat",
|
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"output_cost_per_token": 2e-07,
|
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"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
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},
|
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"nscale/meta-llama/Llama-4-Scout-17B-16E-Instruct": {
|
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"input_cost_per_token": 9e-08,
|
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"litellm_provider": "nscale",
|
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"mode": "chat",
|
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"output_cost_per_token": 2.9e-07,
|
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"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
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},
|
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"nscale/mistralai/mixtral-8x22b-instruct-v0.1": {
|
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|
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"litellm_provider": "nscale",
|
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"metadata": {
|
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"notes": "Pricing listed as $1.20/1M tokens total. Assumed 50/50 split for input/output."
|
|
},
|
|
"mode": "chat",
|
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"output_cost_per_token": 6e-07,
|
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"source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
|
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},
|
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"nscale/stabilityai/stable-diffusion-xl-base-1.0": {
|
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|
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"litellm_provider": "nscale",
|
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"mode": "image_generation",
|
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"output_cost_per_pixel": 0.0,
|
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"source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models",
|
|
"supported_endpoints": [
|
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"/v1/images/generations"
|
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|
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|
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"nebius/deepseek-ai/DeepSeek-R1": {
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|
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|
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|
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|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"supports_reasoning": true,
|
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"source": "https://nebius.com/prices-ai-studio"
|
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|
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"nebius/deepseek-ai/DeepSeek-R1-0528": {
|
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|
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"max_input_tokens": 164000,
|
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|
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"output_cost_per_token": 2.4e-06,
|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"supports_reasoning": true,
|
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"source": "https://nebius.com/prices-ai-studio"
|
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|
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|
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|
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|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"source": "https://nebius.com/prices-ai-studio"
|
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"nebius/deepseek-ai/DeepSeek-V3": {
|
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|
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"max_input_tokens": 128000,
|
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|
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|
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"output_cost_per_token": 1.5e-06,
|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"source": "https://nebius.com/prices-ai-studio"
|
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"nebius/deepseek-ai/DeepSeek-V3-0324": {
|
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"max_tokens": 128000,
|
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|
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|
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|
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"output_cost_per_token": 1.5e-06,
|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"source": "https://nebius.com/prices-ai-studio"
|
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"max_input_tokens": 128000,
|
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|
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"output_cost_per_token": 2e-07,
|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"supports_vision": true,
|
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"source": "https://nebius.com/prices-ai-studio"
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|
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"max_input_tokens": 128000,
|
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|
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"output_cost_per_token": 4e-07,
|
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"litellm_provider": "nebius",
|
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"mode": "chat",
|
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"supports_function_calling": true,
|
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"source": "https://nebius.com/prices-ai-studio"
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|
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|
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|
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|
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"mode": "chat",
|
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"source": "https://nebius.com/prices-ai-studio"
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"litellm_provider": "nebius",
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|
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"source": "https://nebius.com/prices-ai-studio"
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