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?" |
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?" |
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 Plan use (Claude, ChatGPT) assumes a plan runs down in step with API prices, because plans do not publish per-model limits.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_historysays 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_DATAto 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.
Metadata
Release files for llmledger-mcp 0.1.3
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| File | Size | Uploaded | Provenance | |
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|---|---|---|---|---|
| llmledger_mcp-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 641.7 kB
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