- Add model identifier to FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS
- Add imageSize parameter support (1K, 2K, 4K) with GeminiImageSize type
- Add tests for imageSize parameter transformation
- Update documentation with new model
Implements support for reasoning_effort="none" parameter for Gemini models,
providing significant cost savings (up to 96% cheaper) by disabling thinking
budget while maintaining response quality.
Changes:
- Added "supports_reasoning": true to gemini-2.0-flash-thinking-exp-01-21 in model config
- Implemented mapping for reasoning_effort="none" to thinkingConfig {thinkingBudget: 0, includeThoughts: false}
- Added unit test to verify the mapping works correctly
Performance impact:
- Without reasoning_effort: ~313 tokens
- With reasoning_effort="none": ~12 tokens (96% cheaper)
Closes#16420
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Fixes#16533
Before this fix, non-ASCII characters (Japanese, Spanish, Chinese, etc.)
in function call arguments were being escaped as Unicode sequences.
Example:
- Before: "やあ" → "\u3084\u3042"
- After: "やあ" → "やあ" (preserved)
Changes:
- Add ensure_ascii=False to json.dumps() in _transform_parts()
- Add test for Japanese and Spanish Unicode character preservation
This is not a breaking change as both formats are equivalent in JSON.
The fix improves readability and aligns with OpenAI's behavior.
- Add response_modalities configuration to request format
- Fix response parsing to use camelCase 'inlineData' instead of snake_case 'inline_data'
- Update test to validate proper request format and response parsing
- All existing Gemini image generation tests pass
- Reverted GEMINI_2_5_FLASH_IMAGE_PREVIEW_MODEL constant usage
- Made endpoint selection conditional for gemini-2.5-flash-image-preview only
- Preserved existing Imagen models functionality with :predict endpoint
- Fixed potential breaking change that would affect 6 other Gemini image models
- Add ttl parameter to cache_control for Gemini models
- Support Google's TTL format (e.g., '3600s', '7200s')
- Implement robust TTL extraction and validation
- Extract TTL before system message transformation to handle all cases
- Add comprehensive test suite with 17 test cases in tests/test_litellm/
- Update documentation with TTL usage examples
- Maintain backward compatibility with existing cache_control usage
Fixes#9810
* fix(batches_endpoints/endpoints.py): support passing target model names for batch list as a query param
Fixes issue where cloud run fails calls because GET can't contain request body
* test(test_openai_batches_endpoints.py): add unit test
* docs(managed_batches.md): update docs
* feat(spend_tracking_utils.py): support STORE_PROMPTS_IN_SPEND_LOGS env var
ensures prompt is stored in spend logs
* fix(streaming_iterator.py): fix anthropic - completion streaming iterator to yield content block stop
ensures claude code renders messages
* test: skip local test
