- Add whitelist-based filtering for anthropic_beta headers
- Only allow Bedrock-supported beta flags (computer-use, tool-search, etc.)
- Filter out unsupported flags like mcp-servers, structured-outputs
- Remove output_format parameter from Bedrock Invoke requests
- Force tool-based structured outputs when response_format is used
Fixes#16726
* feat(bedrock): add OpenAI-compatible service_tier parameter translation
Translates OpenAI's service_tier parameter (string) to Bedrock's
serviceTier format (object with type field).
* docs(bedrock): add OpenAI-compatible service_tier parameter documentation
Document the automatic translation from OpenAI-style service_tier
parameter to Bedrock's native serviceTier format.
* feat(bedrock): add service_tier to response when present
According to OpenAI's API documentation, when service_tier is sent in the
request, it should be returned in the response. This commit implements
this behavior for Bedrock Converse API to maintain compatibility with
OpenAI's API.
Changes:
- Added serviceTier field to ConverseResponseBlock type definition
- Moved ServiceTierBlock definition before ConverseResponseBlock to fix
type reference order
- Added response transformation to map Bedrock serviceTier (object) to
OpenAI service_tier (string format)
- Added 4 new tests for response transformation with service_tier
The service_tier is only added to the response when present in Bedrock's
response, maintaining backward compatibility.
Add support for the Bedrock Converse API serviceTier parameter to allow
specifying processing tier (priority, default, or flex).
Changes:
- Add ServiceTierBlock type in litellm/types/llms/bedrock.py
- Add serviceTier to CommonRequestObject
- Add serviceTier to get_config_blocks() in AmazonConverseConfig
- Add comprehensive tests for serviceTier functionality
- Add documentation for serviceTier usage
This allows users to configure service tier via:
- litellm_params in proxy config
- optional_params in SDK calls
* Addd v2/chat support for cohere
* fix streaming
* Use v2_transformation for logging passthrough:
* Use v2_transformation for logging passthrough:
* Add test for checking if document and citation_options is getting passed
* Update the cohere model
* Add cost tracking for vertex ai passthrough batch jobs
* Add full passthrough support
* refactor code according to the comments
* Add passthrough handler
* remove invalid params
* Updated documentation
* Updated documentation
* Updated documentation
* Correct the import
* Add openai videos generation and retrieval support
* add retrieval endpoint
* Add docs
* Add imports
* remove orjson
* remove double import
* fix openai videos format
* remove mock code
* remove not required comments
* Add tests
* Add tests
* Add other video endpoints
* Fix cost calculation and transformation
* Fixed mypy tests
* remove not used imports
* fix documentation for get batch req (#15742)
* Add grounding info to responses API (#15737)
* Add grounding info to responses API
* fix lint errors
* Use typed objects for annotations
* Use typed objects for annotations
* fix mypy error
* Litellm fix json serialize alreting 2 (#15741)
* fix json serializable error for alerts
* Add test
* fix mypt errors
* fix mypt errors
* Add Qwen3 imported model support for AWS Bedrock (#15783)
* Add qwen imported model support
* fix mypy errors
* fix empty user message error (#15784)
* fix typed dict for list
* Add azure supported videos endpoint
* fix mapped tests
* add azure sora models to model map
* Add OpenAI video generation and content retrieval support (#15745)
* Add openai videos generation and retrieval support
* add retrieval endpoint
* Add docs
* Add imports
* remove orjson
* remove double import
* fix openai videos format
* remove mock code
* remove not required comments
* Add tests
* Add tests
* Add other video endpoints
* Fix cost calculation and transformation
* Fixed mypy tests
* remove not used imports
* fix typed dict for list
* fix mypy errors
* move directory
* make v2 chat default
* Fix mypy tests
* Fix mypy tests
* Fix mypy tests
* Fix mypy tests
* Revert "Add Azure Video Generation Support with Sora Integration"
* refactor videos repo
* add test
* Add azure openai videos support
* Add azure openai videos support
* Add router endpoint support for videos
* fix mypy error
* add azure models
* fix mapped test
* fix mypy error
* Add proxy router test
* Add proxy router test
* remove deprecated model name from tests
* fix import error
* fix import error
* Add gaurdrail integration in videos endpoint
* Add logging support for videos endpoint
* Add final documentation supporting videos integration
* fix model name and document input
* Update literals to avoid mypy errors
* Remove unused imports and print statements
* revert guardrail support for video generation and video remix
* revert guardrail support for video generation and video remix
* Fix failing mapped and llm translation tests
Fixes#15263
This PR fixes the cost calculation for Bedrock Anthropic models with prompt caching.
**Root Cause:**
PR #9838 incorrectly removed adding `cacheWriteInputTokens` to `prompt_tokens`
for Bedrock, based on the assumption that it would cause double counting (similar
to an Anthropic API issue). However, Bedrock's token structure is different:
- **Bedrock API**: `inputTokens`, `cacheReadInputTokens`, and `cacheWriteInputTokens`
are ALL separate values that should be summed for total input tokens
- **Anthropic API**: Same structure - all three token types are separate
The fix in #9838 was later reverted for Anthropic (correctly re-adding
`cache_creation_input_tokens` to `prompt_tokens`), but Bedrock was never fixed.
**Changes:**
1. Re-add `cacheWriteInputTokens` to `input_tokens` in Bedrock transformation
2. Update test assertions to reflect correct behavior
3. Add regression test for prompt caching cost calculation
4. Fix typo in Anthropic transformation where `cache_creation_tokens` was
incorrectly set to `cache_read_input_tokens`
**Testing:**
- All existing Bedrock transformation tests pass
- New test validates correct cost calculation with prompt caching
- Verified costs are non-negative and accurate
- Change parameter from request_metadata to requestMetadata to match camelCase convention
- Consistent with guardrailConfig and performanceConfig naming pattern
- Update all references in transformation code and error messages
- Update tests and documentation to use correct parameter name
- Fix type checking for parameter validation
- Test requestMetadata parameter support in get_supported_openai_params
- Test transformation to top-level field in Bedrock API request
- Test validation of AWS constraints: max 16 items, key/value length limits
- Test character set validation for keys and values
- Cover edge cases including empty values and special characters
- Ensure compatibility with existing test patterns
- test_assistant_message_cache_control: Tests assistant messages with string content and cache_control
- test_assistant_message_list_content_cache_control: Tests assistant messages with list content and cache_control
- test_tool_message_cache_control: Tests tool messages with list content and cache_control
- test_tool_message_string_content_cache_control: Tests tool messages with string content and cache_control
- test_assistant_tool_calls_cache_control: Tests assistant tool_calls with cache_control
- test_multiple_tool_calls_with_mixed_cache_control: Tests multiple tool calls with mixed cache_control
- test_no_cache_control_no_cache_point: Tests that messages without cache_control don't generate cachePoint blocks
These tests ensure that cachePoint blocks are correctly generated for all message types when cache_control is present.
- Updated test cases to use the renamed `AmazonAnthropicClaudeConfig` instead of `AmazonAnthropicClaude3Config` for consistency with recent changes.
- Adjusted imports and assertions in test files to reflect the new configuration class name.
* build(model_prices_and_context_window.json): mark all gemini-2.5 models as supporting pdf input
Closes https://github.com/BerriAI/litellm/issues/11881
* fix(anthropic_transformation.py): set custom llm provider custom property
Fixes https://github.com/BerriAI/litellm/issues/11861
* test: add unit test for checking supports_reasoning
* test: add test for vertex ai flow
* feat(bedrock/anthropic): ensure thinking param correctly passed for bedrock/invoke
