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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

Tool For example
suggest_for_project "Which model and effort for this project?" with the starting prompt; pay can be claude or chatgpt to compare within a plan
compare_models "Opus 5.5 or Fable 5.1 for coding, and when is Fable cheaper?"
estimate_cost "What do 2,000 calls a day of 10K tokens in and 1K out cost on these three?", and with cached_tokens and cache_write_tokens, how much the cache saves
find_models "The best rated coding models under $10 per 1M answer tokens that read images"
price_history "Has Claude Opus 5.5's price changed, and how likely is it to change soon?"; with date, "What did it cost on 1 March?" Each change links the LiteLLM change behind it
not_yet_priced "Which new models on Arena have no price yet, and how long does a price usually take?"
estimate_time "How long will 5,000 calls of 2K tokens in and 600 out take on Opus 5.5 through the API, 4 at a time?" Learned from your own runs
log_run "I timed this call: 2,100 in, 640 out, 9.2 seconds", or a promptfoo results file; kept on your computer only

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.
  • 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.

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