The test_chat_completion_low_budget test was flaky because async spend
tracking couldn't reliably catch up within 50 calls with 0.5s sleeps.
Increased to 200 calls with 0.1s sleeps (same total time budget) to
give more opportunities for budget enforcement to trigger.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The recent commit 2a997993d4 replaced httpx.AsyncClient() with
get_async_httpx_client() in ui_sso.py, but the PKCE tests still
patched the old httpx.AsyncClient path. Updated all 10 affected
tests to mock get_async_httpx_client and removed unnecessary
context manager setup since AsyncHTTPHandler is returned directly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
gemini/gemini-2.5-flash lacks cache_creation_input_token_cost in the
model cost map, causing a TypeError when the test multiplies
cache_creation_input_tokens by None. Use claude-haiku-4-5 instead,
which has the required prompt caching cost fields.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix intent params
* Add responses
* fix unrelated test
* test fix - fireworks API endpoint is down
* test fix fireworks ai is having an active outage
* test_completion_cost_databricks
* dbrx fix test API currently not responding
* Update OpenAI Realtime handler to use the correct endpoint and include all query parameters. Adjusted error messages for missing API base and key. Updated health check URL construction to pass model as a query parameter.
* Enhance OpenAI Realtime handler tests to ensure model parameter inclusion in WebSocket URL. Added new tests to verify correct URL construction with model and additional parameters, preventing 'missing_model' errors. Updated existing tests for consistency.
* Remove debug print statements for API base and key in OpenAIRealtime handler to clean up the code.
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(exception_mapping_utils.py): correctly pass through 504 status code
openai also raises a 504 status code
* build(model_prices_and_context_window.json): add gpt-4o-mini-tts to model cost map
Fixes https://github.com/BerriAI/litellm/issues/9591
* fix(cost_calculator.py): fix input cost calculation for gpt-4o-mini-tts
Fixes https://github.com/BerriAI/litellm/issues/9591
* test: testing updates
* fix(cost_calculator.py): handle custom pricing at deployment level for router
* test: add unit tests
* fix(router.py): show custom pricing on UI
check correct model str
* fix: fix linting error
* docs(custom_pricing.md): clarify custom pricing for proxy
Fixes https://github.com/BerriAI/litellm/issues/8573#issuecomment-2790420740
* test: update code qa test
* fix: cleanup traceback
* fix: handle litellm param custom pricing
* test: update test
* fix(cost_calculator.py): add router model id to list of potential model names
* fix(cost_calculator.py): fix router model id check
* fix: router.py - maintain older model registry approach
* fix: fix ruff check
* fix(router.py): router get deployment info
add custom values to mapped dict
* test: update test
* fix(utils.py): update only if value is non-null
* test: add unit test
* fix(vertex_and_google_ai_studio_gemini.py): log gemini audio tokens in usage object
enables accurate cost tracking
* refactor(vertex_ai/cost_calculator.py): refactor 128k+ token cost calculation to only run if model info has it
Google has moved away from this for gemini-2.0 models
* refactor(vertex_ai/cost_calculator.py): migrate to usage object for more flexible data passthrough
* fix(llm_cost_calc/utils.py): support audio token cost tracking in generic cost per token
enables vertex ai cost tracking to work with audio tokens
* fix(llm_cost_calc/utils.py): default to total prompt tokens if text tokens field not set
* refactor(llm_cost_calc/utils.py): move openai cost tracking to generic cost per token
more consistent behaviour across providers
* test: add unit test for gemini audio token cost calculation
* ci: bump ci config
* test: fix test
* fix(core_helpers.py): handle litellm_metadata instead of 'metadata'
* feat(batches/): ensure batches logs are written to db
makes batches response dict compatible
* fix(cost_calculator.py): handle batch response being a dictionary
* fix(batches/main.py): modify retrieve endpoints to use @client decorator
enables logging to work on retrieve call
* fix(batches/main.py): fix retrieve batch response type to be 'dict' compatible
* fix(spend_tracking_utils.py): send unique uuid for retrieve batch call type
create batch and retrieve batch share the same id
* fix(spend_tracking_utils.py): prevent duplicate retrieve batch calls from being double counted
* refactor(batches/): refactor cost tracking for batches - do it on retrieve, and within the established litellm_logging pipeline
ensures cost is always logged to db
* fix: fix linting errors
* fix: fix linting error
* feat(bedrock/rerank): infer model region if model given as arn
* test: add unit testing to ensure bedrock region name inferred from arn on rerank
* feat(bedrock/rerank/transformation.py): include search units for bedrock rerank result
Resolves https://github.com/BerriAI/litellm/issues/7258#issuecomment-2671557137
* test(test_bedrock_completion.py): add testing for bedrock cohere rerank
* feat(cost_calculator.py): refactor rerank cost tracking to support bedrock cost tracking
* build(model_prices_and_context_window.json): add amazon.rerank model to model cost map
* fix(cost_calculator.py): bedrock/common_utils.py
get base model from model w/ arn -> handles rerank model
* build(model_prices_and_context_window.json): add bedrock cohere rerank pricing
* feat(bedrock/rerank): migrate bedrock config to basererank config
* Revert "feat(bedrock/rerank): migrate bedrock config to basererank config"
This reverts commit 84fae1f1679a209a3e9cdcea593ed683fdb96acc.
* test: add testing to ensure large doc / queries are correctly counted
* Revert "test: add testing to ensure large doc / queries are correctly counted"
This reverts commit 4337f1657e13a6d35527a400e3be17c11d4b662b.
* fix(migrate-jina-ai-to-rerank-config): enables cost tracking
* refactor(jina_ai/): finish migrating jina ai to base rerank config
enables cost tracking
* fix(jina_ai/rerank): e2e jina ai rerank cost tracking
* fix: cleanup dead code
* fix: fix python3.8 compatibility error
* test: fix test
* test: add e2e testing for azure ai rerank
* fix: fix linting error
* test: mark cohere as flaky
* test(test_completion_cost.py): add unit testing to ensure all bedrock models with region name have cost tracked
* feat: initial script to get bedrock pricing from amazon api
ensures bedrock pricing is accurate
* build(model_prices_and_context_window.json): correct bedrock model prices based on api check
ensures accurate bedrock pricing
* ci(config.yml): add bedrock pricing check to ci/cd
ensures litellm always maintains up-to-date pricing for bedrock models
* ci(config.yml): add beautiful soup to ci/cd
* test: bump groq model
* test: fix test
* fix(utils.py): initial commit fixing custom cost tracking
refactors out provider specific model info from `get_model_info` - this was causing custom costs to be registered incorrectly
* fix(utils.py): cleanup `_supports_factory` to check provider info, if model info is None
some providers support features like vision across all models
* fix(utils.py): refactor to use _supports_factory
* test: update testing
* fix: fix linting errors
* test: fix testing
* test(test_completion_cost.py): add sdk test to ensure base model is used for cost tracking
* test(test_completion_cost.py): add sdk test to ensure custom pricing works
* fix(main.py): add base model cost tracking support for embedding calls
Enables base model cost tracking for embedding calls when base model set as a litellm_param
* fix(litellm_logging.py): update logging object with litellm params - including base model, if given
ensures base model param is always tracked
* fix(main.py): fix linting errors
* fix(bedrock/converse_handler.py): fix bedrock region name on async calls
* fix(utils.py): fix split model handling
Fixes bedrock cost calculation when region name is given
* feat(_health_endpoints.py): support health checking datadog integration
Closes https://github.com/BerriAI/litellm/issues/7921
* fix(__init__.py): fix init to exclude pricing-only model cost values from real model names
prevents bad health checks on wildcard routes
* fix(get_llm_provider.py): fix to handle calling bedrock_converse models
* feat(cost_calculator.py): add cost tracking ($0) for openai moderations endpoint
removes sentry cost tracking errors caused by this
* build(teams.tsx): allow assigning teams to orgs
* build(model_prices_and_context_window.json): add gemini-1.5-flash context caching
* fix(context_caching/transformation.py): just use last identified cache point
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(context_caching/transformation.py): pick first contiguous block - handles system message error from google
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(vertex_ai/gemini/): track context caching tokens
* refactor(gemini/): place transformation.py inside `chat/` folder
make it easy for user to know we support the equivalent endpoint
* fix: fix import
* refactor(vertex_ai/): move vertex_ai cost calc inside vertex_ai/ folder
make it easier to see cost calculation logic
* fix: fix linting errors
* fix: fix circular import
* feat(gemini/cost_calculator.py): support gemini context caching cost calculation
generifies anthropic's cost calculation function and uses it across anthropic + gemini
* build(model_prices_and_context_window.json): add cost tracking for gemini-1.5-flash-002 w/ context caching
Closes https://github.com/BerriAI/litellm/issues/6891
* docs(gemini.md): add gemini context caching architecture diagram
make it easier for user to understand how context caching works
* docs(gemini.md): link to relevant gemini context caching code
* docs(gemini/context_caching): add readme in github, make it easy for dev to know context caching is supported + where to go for code
* fix(llm_cost_calc/utils.py): handle gemini 128k token diff cost calc scenario
* fix(deepseek/cost_calculator.py): support deepseek context caching cost calculation
* test: fix test
* fix(factory.py): skip empty text blocks for bedrock user messages
Fixes https://github.com/BerriAI/litellm/issues/7169
* Add support for Gemini 2.0 GoogleSearch tool (#7257)
* Add support for google_search tool in gemini 2.0
* Add/modify tests
* Fix grounding check
* Remove 2.0 grounding test; exclude experimental model in VERTEX_MODELS_TO_NOT_TEST
* Swap order of tools
* DFix formatting
* fix(get_api_base.py): return api base in streaming response
Fixes https://github.com/BerriAI/litellm/issues/7249
Closes https://github.com/BerriAI/litellm/pull/7250
* fix(cost_calculator.py): only set base model to model if not none
Fixes https://github.com/BerriAI/litellm/issues/7223
* fix(cost_calculator.py): enforce stricter order when picking model for cost calculation
* fix(cost_calculator.py): fix '_select_model_name_for_cost_calc' to return model name with region name prefix if provided
* fix(utils.py): fix 'get_model_info()' to handle edge case where model name starts with custom llm provider AND custom llm provider is given
* fix(cost_calculator.py): handle `custom_llm_provider-` scenario
* fix(cost_calculator.py): e2e working tts cost tracking
ensures initial message is passed in, to cost calculator
* fix(factory.py): suppress linting errors
* fix(cost_calculator.py): strip llm provider from model name after selecting cost calc model
* fix(litellm_logging.py): store initial request in 'input' field + accept base_model to be passed in litellm_params directly
* test: handle none env var value in flaky test
* fix(litellm_logging.py): fix linting errors
---------
Co-authored-by: Sam B <samlingx@gmail.com>
* fix(utils.py): fix openai-like api response format parsing
Fixes issue passing structured output to litellm_proxy/ route
* fix(cost_calculator.py): fix whisper transcription cost calc to use file duration, not response time
'
* test: skip test if credentials not found
* fix(main.py): fix retries being multiplied when using openai sdk
Closes https://github.com/BerriAI/litellm/pull/7130
* docs(prompt_management.md): add langfuse prompt management doc
* feat(team_endpoints.py): allow teams to add their own models
Enables teams to call their own finetuned models via the proxy
* test: add better enforcement check testing for `/model/new` now that teams can add their own models
* docs(team_model_add.md): tutorial for allowing teams to add their own models
* test: fix test
yuneng-jiang