Add Model Pricing & Context Window
To add pricing or context window information for a model, simply make a PR to this file:
model_prices_and_context_window.json
Sample Spec​
Here's the full specification with all available fields:
{
"sample_spec": {
"aliases": ["optional list of alternate names for this model, e.g. dated versions like sample_spec-20250101"],
"code_interpreter_cost_per_session": 0.0,
"computer_use_input_cost_per_1k_tokens": 0.0,
"computer_use_output_cost_per_1k_tokens": 0.0,
"deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD",
"file_search_cost_per_1k_calls": 0.0,
"file_search_cost_per_gb_per_day": 0.0,
"input_cost_per_audio_token": 0.0,
"input_cost_per_token": 0.0,
"litellm_provider": "one of https://docs.litellm.ai/docs/providers",
"max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
"max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
"max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.",
"mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search",
"output_cost_per_reasoning_token": 0.0,
"output_cost_per_token": 0.0,
"search_context_cost_per_query": {
"search_context_size_high": 0.0,
"search_context_size_low": 0.0,
"search_context_size_medium": 0.0
},
"supported_regions": [
"global",
"us-west-2",
"eu-west-1",
"ap-southeast-1",
"ap-northeast-1"
],
"supports_audio_input": true,
"supports_audio_output": true,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": true,
"supports_vision": true,
"supports_web_search": true,
"vector_store_cost_per_gb_per_day": 0.0
}
}
Batch pricing keys​
Batch API lines bill from the *_batches keys when the entry carries them. An entry without them bills half its standard rate
| Key | Bills |
|---|---|
input_cost_per_token_batches | prompt tokens |
output_cost_per_token_batches | completion tokens |
cache_read_input_token_cost_batches | cached prompt tokens |
cache_creation_input_token_cost_batches | cache write tokens |
input_cost_per_token_above_272k_tokens_batches | prompt tokens, prompt over 272K |
output_cost_per_token_above_272k_tokens_batches | completion tokens, prompt over 272K |
cache_read_input_token_cost_above_272k_tokens_batches | cached prompt tokens, prompt over 272K |
cache_creation_input_token_cost_above_272k_tokens_batches | cache write tokens, prompt over 272K |
The above_272k keys apply to a batch line whose prompt exceeds 272K tokens, the long-context threshold OpenAI prices from. Any tier key the entry leaves out falls back to that entry's flat *_batches rate for the same tokens
Examples​
Anthropic Claude​
{
"claude-3-5-haiku-20241022": {
"cache_creation_input_token_cost": 1e-06,
"cache_creation_input_token_cost_above_1hr": 6e-06,
"cache_read_input_token_cost": 8e-08,
"deprecation_date": "2025-10-01",
"input_cost_per_token": 8e-07,
"litellm_provider": "anthropic",
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 4e-06,
"search_context_cost_per_query": {
"search_context_size_high": 0.01,
"search_context_size_low": 0.01,
"search_context_size_medium": 0.01
},
"supports_assistant_prefill": true,
"supports_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_vision": true
}
}
Vertex AI Gemini​
{
"vertex_ai/gemini-3-pro-preview": {
"cache_read_input_token_cost": 2e-07,
"cache_read_input_token_cost_above_200k_tokens": 4e-07,
"cache_creation_input_token_cost_above_200k_tokens": 2.5e-07,
"input_cost_per_token": 2e-06,
"input_cost_per_token_above_200k_tokens": 4e-06,
"input_cost_per_token_batches": 1e-06,
"litellm_provider": "vertex_ai",
"max_audio_length_hours": 8.4,
"max_audio_per_prompt": 1,
"max_images_per_prompt": 3000,
"max_input_tokens": 1048576,
"max_output_tokens": 65535,
"max_pdf_size_mb": 30,
"max_tokens": 65535,
"max_video_length": 1,
"max_videos_per_prompt": 10,
"mode": "chat",
"output_cost_per_token": 1.2e-05,
"output_cost_per_token_above_200k_tokens": 1.8e-05,
"output_cost_per_token_batches": 6e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_prompt_caching": true,
"supports_system_messages": true,
"supports_vision": true
}
}
Using Aliases​
Many providers release the same model under multiple names, for example a latest tag and a dated version like claude-sonnet-4-5-20250929. Instead of duplicating the entire entry, you can use the aliases field:
{
"claude-sonnet-4-5": {
"aliases": ["claude-sonnet-4-5-20250929"],
"input_cost_per_token": 3e-06,
"output_cost_per_token": 1.5e-05,
"litellm_provider": "anthropic",
"max_input_tokens": 200000,
"max_output_tokens": 64000,
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true
}
}
At load time, each alias is expanded into a top-level entry sharing the same data as the canonical entry. The example above makes both claude-sonnet-4-5 and claude-sonnet-4-5-20250929 resolve with the same pricing and capabilities.
This is different from model_alias_map, which is a runtime SDK/proxy feature for mapping user-facing model names to LiteLLM model identifiers. The aliases field here is for the model cost JSON only, and it avoids duplicate entries for models that share identical pricing and capabilities.