mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-12 23:01:41 +00:00
feat(auto-update): add native Anthropic models source for day-one coverage
The weekly price/context auto-updater only pulled from OpenRouter and Vercel AI Gateway and only wrote openrouter/* and vercel_ai_gateway/* keys, so the native anthropic provider keys (e.g. claude-opus-4-8) were maintained by hand and lagged new releases. Add Anthropic's /v1/models as a source. It is the origin of truth for model existence, context window, and capabilities, so it is at least as fresh as any aggregator. Since that endpoint returns no pricing, the per-token price is copied from the OpenRouter data the script already fetches, matched on a normalized model id. A new model is only added when a price match is found, so we never introduce a silent zero-cost entry, and existing curated entries are never overwritten. Gated on ANTHROPIC_API_KEY so forks and local runs skip the source cleanly.
This commit is contained in:
mateo-berri
parent
64d8d7f8cb
commit
8c841b0f74
3 changed files with 378 additions and 50 deletions
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@ -23,7 +23,9 @@ jobs:
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version: "0.10.9"
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- name: Update JSON Data
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run: |
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uv run --frozen --with 'aiohttp==3.13.3' python ".github/workflows/auto_update_price_and_context_window_file.py"
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uv run --frozen --with 'aiohttp==3.13.3' --with 'pydantic==2.13.4' python ".github/workflows/auto_update_price_and_context_window_file.py"
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env:
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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- name: Create Pull Request
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run: |
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git add model_prices_and_context_window.json
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@ -1,6 +1,10 @@
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import asyncio
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import os
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import re
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import aiohttp
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import json
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from pydantic import BaseModel
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# Asynchronously fetch data from a given URL
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async def fetch_data(url):
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@ -15,22 +19,24 @@ async def fetch_data(url):
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resp_json = await resp.json()
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print("Fetch the data from URL.")
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# Return the 'data' field from the JSON response
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return resp_json['data']
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return resp_json["data"]
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except Exception as e:
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# Print an error message if fetching data fails
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print("Error fetching data from URL:", e)
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return None
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# Synchronize local data with remote data
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def sync_local_data_with_remote(local_data, remote_data):
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# Update existing keys in local_data with values from remote_data
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for key in (set(local_data) & set(remote_data)):
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for key in set(local_data) & set(remote_data):
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local_data[key].update(remote_data[key])
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# Add new keys from remote_data to local_data
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for key in (set(remote_data) - set(local_data)):
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for key in set(remote_data) - set(local_data):
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local_data[key] = remote_data[key]
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# Write data to the json file
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def write_to_file(file_path, data):
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try:
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@ -43,6 +49,7 @@ def write_to_file(file_path, data):
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# Print an error message if writing to file fails
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print("Error updating JSON file:", e)
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# Update the existing models and add the missing models for OpenRouter
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def transform_openrouter_data(data):
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transformed = {}
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@ -54,33 +61,41 @@ def transform_openrouter_data(data):
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}
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# Add 'max_output_tokens' as a field if it is not None
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if "top_provider" in row and "max_completion_tokens" in row["top_provider"] and row["top_provider"]["max_completion_tokens"] is not None:
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obj['max_output_tokens'] = int(row["top_provider"]["max_completion_tokens"])
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if (
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"top_provider" in row
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and "max_completion_tokens" in row["top_provider"]
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and row["top_provider"]["max_completion_tokens"] is not None
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):
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obj["max_output_tokens"] = int(row["top_provider"]["max_completion_tokens"])
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# Add the field 'output_cost_per_token'
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obj.update({
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"output_cost_per_token": float(row["pricing"]["completion"]),
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})
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obj.update(
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{
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"output_cost_per_token": float(row["pricing"]["completion"]),
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}
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)
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# Add field 'input_cost_per_image' if it exists and is non-zero
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if "pricing" in row and "image" in row["pricing"] and float(row["pricing"]["image"]) != 0.0:
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obj['input_cost_per_image'] = float(row["pricing"]["image"])
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if (
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"pricing" in row
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and "image" in row["pricing"]
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and float(row["pricing"]["image"]) != 0.0
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):
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obj["input_cost_per_image"] = float(row["pricing"]["image"])
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# Add the fields 'litellm_provider' and 'mode'
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obj.update({
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"litellm_provider": "openrouter",
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"mode": "chat"
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})
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obj.update({"litellm_provider": "openrouter", "mode": "chat"})
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# Add the 'supports_vision' field if the modality is 'multimodal'
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if row.get('architecture', {}).get('modality') == 'multimodal':
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obj['supports_vision'] = True
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if row.get("architecture", {}).get("modality") == "multimodal":
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obj["supports_vision"] = True
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# Use a composite key to store the transformed object
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transformed[f'openrouter/{row["id"]}'] = obj
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transformed[f"openrouter/{row['id']}"] = obj
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return transformed
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# Update the existing models and add the missing models for Vercel AI Gateway
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def transform_vercel_ai_gateway_data(data):
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transformed = {}
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@ -89,27 +104,181 @@ def transform_vercel_ai_gateway_data(data):
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"max_tokens": row["context_window"],
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"input_cost_per_token": float(row["pricing"]["input"]),
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"output_cost_per_token": float(row["pricing"]["output"]),
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'max_output_tokens': row['max_tokens'],
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'max_input_tokens': row["context_window"],
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"max_output_tokens": row["max_tokens"],
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"max_input_tokens": row["context_window"],
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}
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# Handle cache pricing if available
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if "pricing" in row:
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if "input_cache_read" in row["pricing"] and row["pricing"]["input_cache_read"] is not None:
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obj['cache_read_input_token_cost'] = float(f"{float(row['pricing']['input_cache_read']):e}")
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if "input_cache_write" in row["pricing"] and row["pricing"]["input_cache_write"] is not None:
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obj['cache_creation_input_token_cost'] = float(f"{float(row['pricing']['input_cache_write']):e}")
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if (
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"input_cache_read" in row["pricing"]
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and row["pricing"]["input_cache_read"] is not None
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):
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obj["cache_read_input_token_cost"] = float(
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f"{float(row['pricing']['input_cache_read']):e}"
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)
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if (
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"input_cache_write" in row["pricing"]
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and row["pricing"]["input_cache_write"] is not None
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):
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obj["cache_creation_input_token_cost"] = float(
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f"{float(row['pricing']['input_cache_write']):e}"
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)
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mode = "embedding" if "embedding" in row["id"].lower() else "chat"
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obj.update({"litellm_provider": "vercel_ai_gateway", "mode": mode})
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transformed[f'vercel_ai_gateway/{row["id"]}'] = obj
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transformed[f"vercel_ai_gateway/{row['id']}"] = obj
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return transformed
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class _SupportFlag(BaseModel):
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supported: bool = False
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class _ThinkingTypes(BaseModel):
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adaptive: _SupportFlag = _SupportFlag()
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class _Thinking(BaseModel):
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supported: bool = False
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types: _ThinkingTypes = _ThinkingTypes()
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class _Effort(BaseModel):
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supported: bool = False
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xhigh: _SupportFlag = _SupportFlag()
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max: _SupportFlag = _SupportFlag()
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class _Capabilities(BaseModel):
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image_input: _SupportFlag = _SupportFlag()
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pdf_input: _SupportFlag = _SupportFlag()
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structured_outputs: _SupportFlag = _SupportFlag()
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thinking: _Thinking = _Thinking()
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effort: _Effort = _Effort()
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class AnthropicModel(BaseModel):
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id: str
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max_input_tokens: int | None = None
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max_tokens: int | None = None
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capabilities: _Capabilities = _Capabilities()
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class OpenRouterPricing(BaseModel):
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prompt: float
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completion: float
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input_cache_read: float | None = None
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input_cache_write: float | None = None
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class OpenRouterModel(BaseModel):
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id: str
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pricing: OpenRouterPricing
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class AnthropicEntry(BaseModel):
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litellm_provider: str
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mode: str
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max_tokens: int | None = None
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max_input_tokens: int | None = None
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max_output_tokens: int | None = None
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input_cost_per_token: float
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output_cost_per_token: float
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cache_read_input_token_cost: float | None = None
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cache_creation_input_token_cost: float | None = None
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supports_vision: bool | None = None
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supports_pdf_input: bool | None = None
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supports_response_schema: bool | None = None
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supports_reasoning: bool | None = None
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supports_adaptive_thinking: bool | None = None
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supports_xhigh_reasoning_effort: bool | None = None
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supports_max_reasoning_effort: bool | None = None
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supports_function_calling: bool | None = None
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supports_tool_choice: bool | None = None
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supports_prompt_caching: bool | None = None
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# Normalize a provider model id to a comparable key (drop prefix/suffix, dates, dot vs dash)
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def canonical_model_id(model_id):
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base = model_id.split("/")[-1].split(":")[0].lower()
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base = re.sub(r"-\d{8}$", "", base)
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return base.replace(".", "-")
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# Build a price lookup from OpenRouter's Anthropic models, preferring base ids over ":variant" ids
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def build_anthropic_price_index(openrouter_rows):
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anthropic_rows = (
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OpenRouterModel.model_validate(row)
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for row in openrouter_rows
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if str(row.get("id", "")).startswith("anthropic/")
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)
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return {
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canonical_model_id(model.id): model.pricing
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for model in sorted(anthropic_rows, key=lambda model: ":" not in model.id)
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}
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# Build native Anthropic entries for models that are new and have a known price
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def transform_anthropic_data(anthropic_rows, price_index, existing_keys):
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entries = {}
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for row in anthropic_rows:
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model = AnthropicModel.model_validate(row)
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if model.id in existing_keys:
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continue
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pricing = price_index.get(canonical_model_id(model.id))
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if pricing is None:
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print(f"Skipping {model.id}: no OpenRouter price match")
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continue
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caps = model.capabilities
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entry = AnthropicEntry(
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litellm_provider="anthropic",
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mode="chat",
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max_tokens=model.max_tokens,
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max_input_tokens=model.max_input_tokens,
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max_output_tokens=model.max_tokens,
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input_cost_per_token=pricing.prompt,
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output_cost_per_token=pricing.completion,
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cache_read_input_token_cost=pricing.input_cache_read,
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cache_creation_input_token_cost=pricing.input_cache_write,
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supports_vision=caps.image_input.supported or None,
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supports_pdf_input=caps.pdf_input.supported or None,
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supports_response_schema=caps.structured_outputs.supported or None,
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supports_reasoning=caps.thinking.supported or None,
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supports_adaptive_thinking=caps.thinking.types.adaptive.supported or None,
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supports_xhigh_reasoning_effort=caps.effort.xhigh.supported or None,
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supports_max_reasoning_effort=caps.effort.max.supported or None,
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supports_function_calling=True,
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supports_tool_choice=True,
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supports_prompt_caching=(pricing.input_cache_read is not None) or None,
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)
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entries[model.id] = entry.model_dump(exclude_none=True)
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return entries
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# Fetch the Anthropic models list (authoritative for existence, context window, capabilities)
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async def fetch_anthropic_models(api_key):
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headers = {"x-api-key": api_key, "anthropic-version": "2023-06-01"}
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url = "https://api.anthropic.com/v1/models?limit=1000"
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(url, headers=headers) as resp:
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resp.raise_for_status()
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payload = await resp.json()
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if payload.get("has_more"):
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print(
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"Warning: Anthropic models response truncated; pagination not implemented"
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)
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return payload["data"]
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except Exception as e:
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print("Error fetching Anthropic models:", e)
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return None
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# Load local data from a specified file
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def load_local_data(file_path):
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try:
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@ -126,33 +295,53 @@ def load_local_data(file_path):
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print("Error decoding JSON:", e)
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return None
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def main():
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local_file_path = "model_prices_and_context_window.json" # Path to the local data file
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openrouter_url = "https://openrouter.ai/api/v1/models" # URL to fetch OpenRouter data
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vercel_ai_gateway_url = "https://ai-gateway.vercel.sh/v1/models" # URL to fetch Vercel AI Gateway data
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local_file_path = (
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"model_prices_and_context_window.json" # Path to the local data file
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)
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openrouter_url = (
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"https://openrouter.ai/api/v1/models" # URL to fetch OpenRouter data
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)
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vercel_ai_gateway_url = (
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"https://ai-gateway.vercel.sh/v1/models" # URL to fetch Vercel AI Gateway data
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)
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# Load local data from file
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local_data = load_local_data(local_file_path)
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# Fetch OpenRouter data
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openrouter_data = asyncio.run(fetch_data(openrouter_url))
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# Transform the fetched OpenRouter data
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openrouter_data = transform_openrouter_data(openrouter_data)
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# Fetch Vercel AI Gateway data
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vercel_data = asyncio.run(fetch_data(vercel_ai_gateway_url))
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# Transform the fetched Vercel AI Gateway data
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vercel_data = transform_vercel_ai_gateway_data(vercel_data)
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# Combine both datasets
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all_remote_data = {**openrouter_data, **vercel_data}
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if not local_data:
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print("Failed to load local model data.")
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return
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existing_keys = frozenset(local_data)
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openrouter_rows = asyncio.run(fetch_data(openrouter_url))
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vercel_rows = asyncio.run(fetch_data(vercel_ai_gateway_url))
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remote_data = {}
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if openrouter_rows:
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remote_data.update(transform_openrouter_data(openrouter_rows))
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if vercel_rows:
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remote_data.update(transform_vercel_ai_gateway_data(vercel_rows))
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if not remote_data:
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print("Failed to fetch model data from remote URLs.")
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return
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sync_local_data_with_remote(local_data, remote_data)
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anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY")
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if anthropic_api_key and openrouter_rows:
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anthropic_rows = asyncio.run(fetch_anthropic_models(anthropic_api_key))
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if anthropic_rows:
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price_index = build_anthropic_price_index(openrouter_rows)
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for key, entry in transform_anthropic_data(
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anthropic_rows, price_index, existing_keys
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).items():
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local_data.setdefault(key, entry)
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elif not anthropic_api_key:
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print("ANTHROPIC_API_KEY not set; skipping native Anthropic source.")
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write_to_file(local_file_path, local_data)
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# If both local and openrouter data are available, synchronize and save
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if local_data and all_remote_data:
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sync_local_data_with_remote(local_data, all_remote_data)
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write_to_file(local_file_path, local_data)
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else:
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print("Failed to fetch model data from either local file or URL.")
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# Entry point of the script
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if __name__ == "__main__":
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@ -0,0 +1,137 @@
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import importlib.util
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from pathlib import Path
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_MODULE_PATH = (
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Path(__file__).resolve().parents[2]
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/ ".github"
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/ "workflows"
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/ "auto_update_price_and_context_window_file.py"
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)
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def _load_module():
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spec = importlib.util.spec_from_file_location(
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"auto_update_price_and_context_window_file", _MODULE_PATH
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)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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mod = _load_module()
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def _opus_model(model_id="claude-opus-4-9"):
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return {
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"id": model_id,
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"max_input_tokens": 1000000,
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"max_tokens": 128000,
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"capabilities": {
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"image_input": {"supported": True},
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"pdf_input": {"supported": True},
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"structured_outputs": {"supported": True},
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"thinking": {"supported": True, "types": {"adaptive": {"supported": True}}},
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"effort": {
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"supported": True,
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"xhigh": {"supported": True},
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"max": {"supported": True},
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},
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},
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}
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def _openrouter_rows():
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return [
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{
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"id": "anthropic/claude-opus-4.9",
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"pricing": {
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"prompt": "0.000005",
|
||||
"completion": "0.000025",
|
||||
"input_cache_read": "0.0000005",
|
||||
"input_cache_write": "0.00000625",
|
||||
},
|
||||
},
|
||||
{
|
||||
"id": "openai/gpt-5.5",
|
||||
"pricing": {"prompt": "0.000005", "completion": "0.00003"},
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def test_canonical_id_normalizes_dots_dates_and_prefix():
|
||||
assert mod.canonical_model_id("anthropic/claude-opus-4.9") == "claude-opus-4-9"
|
||||
assert mod.canonical_model_id("claude-opus-4-5-20251101") == "claude-opus-4-5"
|
||||
assert (
|
||||
mod.canonical_model_id("anthropic/claude-opus-4.9:thinking")
|
||||
== "claude-opus-4-9"
|
||||
)
|
||||
|
||||
|
||||
def test_price_index_prefers_base_over_variant():
|
||||
rows = [
|
||||
{
|
||||
"id": "anthropic/claude-opus-4.9:thinking",
|
||||
"pricing": {"prompt": "9", "completion": "9"},
|
||||
},
|
||||
{
|
||||
"id": "anthropic/claude-opus-4.9",
|
||||
"pricing": {"prompt": "0.000005", "completion": "0.000025"},
|
||||
},
|
||||
]
|
||||
index = mod.build_anthropic_price_index(rows)
|
||||
assert index["claude-opus-4-9"].prompt == 0.000005
|
||||
assert index["claude-opus-4-9"].completion == 0.000025
|
||||
|
||||
|
||||
def test_new_model_gets_price_context_and_capabilities():
|
||||
index = mod.build_anthropic_price_index(_openrouter_rows())
|
||||
entries = mod.transform_anthropic_data([_opus_model()], index, frozenset())
|
||||
|
||||
assert "claude-opus-4-9" in entries
|
||||
entry = entries["claude-opus-4-9"]
|
||||
assert entry["litellm_provider"] == "anthropic"
|
||||
assert entry["mode"] == "chat"
|
||||
assert entry["max_input_tokens"] == 1000000
|
||||
assert entry["max_output_tokens"] == 128000
|
||||
assert entry["max_tokens"] == 128000
|
||||
assert entry["input_cost_per_token"] == 0.000005
|
||||
assert entry["output_cost_per_token"] == 0.000025
|
||||
assert entry["cache_read_input_token_cost"] == 0.0000005
|
||||
assert entry["cache_creation_input_token_cost"] == 0.00000625
|
||||
assert entry["supports_vision"] is True
|
||||
assert entry["supports_pdf_input"] is True
|
||||
assert entry["supports_reasoning"] is True
|
||||
assert entry["supports_adaptive_thinking"] is True
|
||||
assert entry["supports_response_schema"] is True
|
||||
assert entry["supports_xhigh_reasoning_effort"] is True
|
||||
assert entry["supports_max_reasoning_effort"] is True
|
||||
assert entry["supports_function_calling"] is True
|
||||
assert entry["supports_tool_choice"] is True
|
||||
assert entry["supports_prompt_caching"] is True
|
||||
|
||||
|
||||
def test_model_without_price_match_is_skipped():
|
||||
index = mod.build_anthropic_price_index(_openrouter_rows())
|
||||
entries = mod.transform_anthropic_data(
|
||||
[_opus_model("claude-unlisted-model")], index, frozenset()
|
||||
)
|
||||
assert entries == {}
|
||||
|
||||
|
||||
def test_existing_curated_model_is_not_re_added():
|
||||
index = mod.build_anthropic_price_index(_openrouter_rows())
|
||||
entries = mod.transform_anthropic_data(
|
||||
[_opus_model()], index, frozenset({"claude-opus-4-9"})
|
||||
)
|
||||
assert entries == {}
|
||||
|
||||
|
||||
def test_false_capabilities_are_omitted():
|
||||
model = _opus_model()
|
||||
model["capabilities"]["image_input"]["supported"] = False
|
||||
model["capabilities"]["effort"]["xhigh"]["supported"] = False
|
||||
index = mod.build_anthropic_price_index(_openrouter_rows())
|
||||
entry = mod.transform_anthropic_data([model], index, frozenset())["claude-opus-4-9"]
|
||||
assert "supports_vision" not in entry
|
||||
assert "supports_xhigh_reasoning_effort" not in entry
|
||||
assert entry["supports_pdf_input"] is True
|
||||
Loading…
Add table
Reference in a new issue