llmledger-mcp

Ask Claude (or Cursor, ChatGPT desktop, any app that speaks MCP) which AI model to use and what it costs, in the middle of your work. The answers come from llmledger: LiteLLM's public price catalogue, Arena's ratings by task, and every price change since 2023 with how likely a price is to change next. Rebuilt every day.

Install

Needs uv. Nothing to host and no keys: the plug-in runs on your computer and reads the day's data from the llmledger page, cached for a day, with a bundled copy for offline use.

Claude Code:

claude mcp add llmledger -- uvx llmledger-mcp

Claude Desktop (Settings, Developer, Edit config) or Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "llmledger": { "command": "uvx", "args": ["llmledger-mcp"] }
  }
}

A LiteLLM proxy can offer it to a whole team through its MCP gateway, as a stdio server with the same command.

The same answers also come as an Agent Skill, which takes far less of an agent's context until it is used and needs only Python: skills/llmledger. In Claude Code: /plugin marketplace add pouriaetab/llmledger, then /plugin install llmledger@llmledger.

What you can ask

Plus a resource, llmledger://snapshot (data dates and sources), and a prompt, pick_a_model.

Good to know

• Costs and ratings are estimates. Arena ratings say which answers people preferred, not what will work best for you. Plan use (Claude, ChatGPT) assumes a plan runs down in step with API prices, because plans do not publish per-model limits. Not financial or procurement advice.
• Effort levels come from LiteLLM; the suggested level is a rule of thumb. No open source measures tokens per task at each effort level yet.
• Many price changes in the catalogue fix a wrong entry rather than change a price: in a labelled sample, about 6 in 10 first price changes. price_history says what each flag on a change means (a likely correction, a unit fix, a change undone) and how often such changes were corrections.
• Every answer carries the date of its data. When the data is 3 or more days old, the answer says so.
• Times come only from your own runs (and the sample's own timed calls for forecast_job), always as a usual time and a slow day. How often a model was right is the share of a sample's checks it passed, with a 95% range; with 20 tests the range is wide, and the answer says when two models cannot be told apart.
• Set LLMLEDGER_DATA to a local data.json or another address to use your own copy.

Data and licences

Prices: LiteLLM's model_prices_and_context_window.json (MIT). Ratings: the Arena leaderboard (CC BY 4.0). Price history and outlook: llmledger (MIT). Built with FastMCP (Apache-2.0). Independent; not affiliated with BerriAI, LiteLLM or Arena.