* Add Amazon Nova as a first party provider
* Added new provider folder under llms/ to outline the openai supported params
* Updated supported endpoints on the documnetation
When Gemini image generation models return `text_tokens=0` with `image_tokens > 0`,
the cost calculator was assuming no token breakdown existed and treating all
completion tokens as text tokens, resulting in ~10x underestimation of costs.
Changes:
- Fix cost calculation logic to respect token breakdown when image/audio/reasoning
tokens are present, even if text_tokens=0
- Add `output_cost_per_image_token` pricing for gemini-3-pro-image-preview models
- Add test case reproducing the issue
- Add documentation explaining image token pricing
Fixes#17410
Add support for OpenAI's gpt-5.1-codex-max model, their most intelligent
coding model optimized for long-horizon agentic coding tasks.
- 400k context window, 128k max output tokens
- $1.25/1M input, $10/1M output, $0.125/1M cached input
- Only available via /v1/responses endpoint
- Supports vision, function calling, reasoning, prompt caching
Fixes issue where LiteLLM used the json_tool_call workaround for all Groq
models, even those that support native json_schema (e.g., gpt-oss-120b,
llama-4, kimi-k2). This caused errors when users passed their own tools
alongside structured outputs.
Changes:
- Check `supports_response_schema()` before using the workaround
- Only use json_tool_call workaround for models without native support
- Add clear error message when using workaround with user-provided tools
- Update model config to set `supports_response_schema: false` for models
that don't support native json_schema
- Add unit tests for structured output handling
* docs vertex tts
* place vertex ai types in file
* use VertexAITextToSpeechConfig
* use vertex_voice_dict
* refactor docs
* docs vertex ai chirp
* TestVertexAITextToSpeechConfig
* new provider vertex ai chirp3
* test_litellm_speech_vertex_ai_chirp
* add vertex_ai/chirp cost trackign
* update databricks pricing and add DBU<>USD test
* Refactor test_databricks_pricing.py
Removed unnecessary sys.path modification and cleaned up comments.
* add claude opus 4.5
* Apply suggestion from @Chesars
Co-authored-by: Cesar Garcia <128240629+Chesars@users.noreply.github.com>
---------
Co-authored-by: Cesar Garcia <128240629+Chesars@users.noreply.github.com>
Add gemini-3-pro-image-preview model configuration for Google's new
image generation model (aka "Nano Banana Pro 🍌").
Model details:
- Input: $2.00/1M tokens (text), $0.0011/image
- Output: $12.00/1M tokens (text), $0.134/image (1K/2K)
- Context: 65k input / 32k output tokens
- Capabilities: structured outputs, web search, caching, thinking
- No function calling support
- Available on both Gemini API and Vertex AI
Added variants:
- gemini-3-pro-image-preview (base, uses Vertex AI)
- gemini/gemini-3-pro-image-preview (Gemini API)
- vertex_ai/gemini-3-pro-image-preview (Vertex AI)
Source: https://ai.google.dev/gemini-api/docs/pricingFixes: #16925
Change model identifier from cerebras/openai/gpt-oss-120b to
cerebras/gpt-oss-120b to match Cerebras API requirements.
The Cerebras API only accepts 'gpt-oss-120b' as the model ID, not
'openai/gpt-oss-120b'. The previous name was causing "Model does not
exist" errors when users tried to use it.
Tested with real API calls to confirm:
- cerebras/gpt-oss-120b → sends 'gpt-oss-120b' → ✅ works
- cerebras/openai/gpt-oss-120b → sends 'openai/gpt-oss-120b' → ❌ fails
Fixes#16924
* Add thought signature support to v1/messages api
* update the thinking level handling logic
* update the thinking level handling logic
* Add streaming support
* fix intalling litellm error
