P
pouriaetab/llmledger
Which AI model should you use, and what will it cost? Every price change in LiteLLM's catalogue since 2023, Arena ratings, a competing risks model, an MCP plug-in and an Agent Skill. Rebuilt daily.
无 SECURITY.md
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
- 复制上面的命令
- 粘成
claude_desktop_config.json里mcpServers的一项(本地服务器用 command + args,示例见 客户端接法) - 重启客户端
取自该仓库的安装命令
claude mcp add llmledger -- uvx llmledger-mcp
解析自我们于 2026-10-26 抓取的该仓库 README;我们未执行过。
这条是仓库自己写的安装方式,不是 MCP 客户端配置,所以不生成 JSON。
配置项的键名以你的客户端为准:客户端与 CLI
- ★ 数取值
- 0 ★ · pouriaetab/llmledger · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
- 收录与活跃
- 收录于 2026-10-23 · 最近见到 2026-11-14 · 共见到 26 次 · 被 23 个来源列出
- 评分构成
- 综合分 2,300 · 23 个来源 · mcp
- 安装
claude mcp add llmledger -- uvx llmledger-mcp
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是
claude mcp add llmledger -- uvx llmledger-mcp。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库
pouriaetab/llmledger;我们收录于 2026-10-23;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。# llmledger
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
Which AI model should you use, and what will it cost? Paste a prompt and get the best value
model for it, the top rated one and the cheapest rated one, with what each costs and the chance
its price changes in the next 90 days. Behind it: every price change in LiteLLM's model catalogue
since 2023 (4,200+ models), Arena's quality ratings, and a competing risks survival model, rebuilt
every day.
Try it: https://pouriaetab.github.io/llmledger/ (no sign up, the prompt stays in your browser)
## Use it inside Claude
The same answers inside Claude Code, Claude Desktop, Cursor or any app that speaks MCP. Nothing to host, no keys:
bash
claude mcp add llmledger -- uvx llmledger-mcp
Eleven tools: suggest a model and effort level for a project, compare models side by side, estimate a budget, find
models within limits, a model's price history with its outlook, new models not priced yet, how long a job takes (learned
from your own runs), a quote for a whole job from a promptfoo sample, whether buying a computer to run an open model
pays off, and what your own coding agent usage (a ccusage or codeburn export) would have cost on every other model.
Other apps and details: mcp/README.md.
As an Agent Skill, which stays out of the context until it is used (about 150 tokens against well over 1,000 for the
plug-in's tools) and needs only Python:
text
/plugin marketplace add pouriaetab/llmledger
/plugin install llmledger@llmledger
or npx skills add pouriaetab/llmledger, or copy skills/llmledger into your agent's skills folder.
## Watch the models you use
A GitHub Action that fails a check (or just reports) when a model your project uses changes price, gets a retirement
date or leaves the catalogue. On the GitHub Marketplace, or
add it to a workflow that runs on a schedule:
yaml
uses: pouriaetab/llmledger@main
with:
models: gpt-5, claude-sonnet-4-5, deepseek/deepseek-chat
days: 7 # a price change within this many days counts
fail: "true" # "false" only reports; the report is in the step's outputs
Keys are LiteLLM's (gpt-5, not openai/gpt-5; the Action finds the bare key when a prefix is given). The same check
from a shell: python action/watch.py --models "gpt-5,claude-sonnet-4-5".
A badge for a model, for any README, refreshed every day:
markdown
The address is the key with / as -- and : as - (badge/azure--eu--gpt-5.json). It shows the prices per 1M
tokens and the chance of a price change in 90 days (amber from 10%), or the retirement date (orange).
## What you get
Ask. Paste a prompt, see the best value model, the top rated and the cheapest rated for its task (coding, math,
writing, 7 fields, 7 languages, images, documents, websites), with the cost per answer and the chance of a price
change. The task is guessed from the prompt, in the browser.
Compare. Up to five models side by side: Arena rating, prices, cost per task, effort levels, or how fast each uses
a Claude or ChatGPT plan. Cost against quality, with the models no other model beats on both.
Models. Every model in the catalogue, as LiteLLM lists it or grouped by model, with its price history, every
change linked to the LiteLLM commit behind it, and its outlook.
Trends. Two years of Arena leaderboards replayed: the best open model against the best closed one, the top
model for it, the top rated one and the cheapest rated one, with what each costs and the chance
its price changes in the next 90 days. Behind it: every price change in LiteLLM's model catalogue
since 2023 (4,200+ models), Arena's quality ratings, and a competing risks survival model, rebuilt
every day.
Try it: https://pouriaetab.github.io/llmledger/ (no sign up, the prompt stays in your browser)
## Use it inside Claude
The same answers inside Claude Code, Claude Desktop, Cursor or any app that speaks MCP. Nothing to host, no keys:
bash
claude mcp add llmledger -- uvx llmledger-mcp
Eleven tools: suggest a model and effort level for a project, compare models side by side, estimate a budget, find
models within limits, a model's price history with its outlook, new models not priced yet, how long a job takes (learned
from your own runs), a quote for a whole job from a promptfoo sample, whether buying a computer to run an open model
pays off, and what your own coding agent usage (a ccusage or codeburn export) would have cost on every other model.
Other apps and details: mcp/README.md.
As an Agent Skill, which stays out of the context until it is used (about 150 tokens against well over 1,000 for the
plug-in's tools) and needs only Python:
text
/plugin marketplace add pouriaetab/llmledger
/plugin install llmledger@llmledger
or npx skills add pouriaetab/llmledger, or copy skills/llmledger into your agent's skills folder.
## Watch the models you use
A GitHub Action that fails a check (or just reports) when a model your project uses changes price, gets a retirement
date or leaves the catalogue. On the GitHub Marketplace, or
add it to a workflow that runs on a schedule:
yaml
uses: pouriaetab/llmledger@main
with:
models: gpt-5, claude-sonnet-4-5, deepseek/deepseek-chat
days: 7 # a price change within this many days counts
fail: "true" # "false" only reports; the report is in the step's outputs
Keys are LiteLLM's (gpt-5, not openai/gpt-5; the Action finds the bare key when a prefix is given). The same check
from a shell: python action/watch.py --models "gpt-5,claude-sonnet-4-5".
A badge for a model, for any README, refreshed every day:
markdown
The address is the key with / as -- and : as - (badge/azure--eu--gpt-5.json). It shows the prices per 1M
tokens and the chance of a price change in 90 days (amber from 10%), or the retirement date (orange).
## What you get
Ask. Paste a prompt, see the best value model, the top rated and the cheapest rated for its task (coding, math,
writing, 7 fields, 7 languages, images, documents, websites), with the cost per answer and the chance of a price
change. The task is guessed from the prompt, in the browser.
Compare. Up to five models side by side: Arena rating, prices, cost per task, effort levels, or how fast each uses
a Claude or ChatGPT plan. Cost against quality, with the models no other model beats on both.
Models. Every model in the catalogue, as LiteLLM lists it or grouped by model, with its price history, every
change linked to the LiteLLM commit behind it, and its outlook.
Trends. Two years of Arena leaderboards replayed: the best open model against the best closed one, the top
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/pouriaetab-llmledger/)
- 来源
- awesome · https://github.com/pouriaetab/llmledger
- 按领域浏览
- MCP 服务
- 收录来源
- 23 个独立来源
- 收录于
- 今天
- 安全策略
- 我们没在该仓库根目录找到
SECURITY.md(这不代表不安全)
同类推荐
所属领域与标签
每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。