com.infercpi由 infercpi.com 发布官方发布者v1.0.0更新于 Oct 29, 2026
AI inference price index: token prices, cost per capability, open vs closed, hosts, cost per task.
只读 MCP 服务器，提供 InferCPI 的 AI 推理价格指数：token 价格、单位能力成本、开源与闭源对比、托管商及单任务成本。
Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。
把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。
阅读 https://sourceweft.com/mcp/com.infercpi%2Fapi/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。把它添加到你客户端的 mcpServers 配置中。
{
"mcpServers": {
"infercpi": {
"type": "http",
"url": "https://infercpi.com/mcp"
}
}
}InferCPI tracks what AI inference costs: list prices, what buyers actually pay, the cost of a fixed level of capability, open vs closed models, third-party hosts, self-hosting, and cost per completed task. Site: https://infercpi.com.
This repo holds the public API docs, examples and the MCP server. The data is free, needs no key, and is licensed CC BY 4.0: cite as InferCPI, infercpi.com.
Static JSON at https://infercpi.com/v1/. Full reference: https://infercpi.com/api.html.
More in examples/.
mcp_server.py gives agents ten read-only tools over the API: index_latest, index_series, index_catalogue, basket, model_price, events, speed, open_vs_closed, self_hosting, cost_per_task.
Hosted, no key: https://infercpi.com/mcp (streamable HTTP).
Claude.ai and Claude Desktop: Settings → Connectors → Add custom connector, paste the URL.
Or run it yourself:
Claude Desktop (claude_desktop_config.json):
It reads https://infercpi.com by default; set INFERCPI_BASE to point elsewhere.
How every series is built: https://infercpi.com/methodology.html. Inputs: LiteLLM and pydantic/genai-prices (MIT), Epoch AI Capabilities Index (CC BY 4.0), OpenRouter rankings (CC BY 4.0), SEC filings (public domain). Speed and cost per task are our own measurements.
Code in this repo: MIT. Index data: CC BY 4.0. Questions, corrections or a series you need: open an issue.
来源：README.md，提交 1e7403e
Latest value of every index series (list-price chained/composition/hedonic, usage-weighted paid price, Törnqvist, OpenRouter shares, TPI replica) with dates, the latest editorial note, and the licence/attribution line. Start here.
无需输入参数。
Full weekly history of one series as [[unix_ts, value], ...] plus its method string. signal: price_index | weighted_index | or_rankings | implied_margin | self_host | task_cost | hf_downloads | gpu_spot | gpu_neocloud | aws_gpu | speed | hyperscaler_capex | seller_revenue | cloud_revenue | token_price ... key: see index_catalogue. since: optional ISO date to cut the history.
key必填sincesignal必填Every series the index publishes: signal/key, point count, first and last date, method string.
无需输入参数。
Every model priced in this week's list-price index: provider, blended $/1M (0.75 in + 0.25 out), Epoch capability score where matched.
无需输入参数。
Priced events in first-party price lists, newest first: launch, price_cut, price_increase, removed, each with key, provider, old/new blended $/1M and pct. since: ISO date; kind: filter to one kind.
kindsinceLatest daily speed run, measured by the index: per model and route (openrouter default routing, openrouter_pinned to the first-party host, anthropic and openai direct), median time to first token (ms), visible output tokens per second, host that served it. Same prompt every day.
无需输入参数。
Blended list $/1M for one model by any common name ('Claude Opus 5.5', 'anthropic/claude-opus-5.5', 'gpt-5.5'); matches by the index's name normaliser and returns every basket entry that matches.
name必填Cheapest blended $/1M at each capability level (Epoch ECI 130/145/160) for closed models, open models at their lab's price and open models at the cheapest third-party host; the open discount; DeepSeek's floor lift (cheapest price without DeepSeek / cheapest overall, at lab and at host prices); and the host discount per open model (lab price / cheapest host).
无需输入参数。
What it costs per 1M tokens to leave the APIs: desk boxes (Mac Studio lease, RTX 5090 bought) at full and 25% duty, H100 rented by source (Vast, AWS, RunPod, CoreWeave, Lambda, Nebius) and owned, against the API price of the same open model and of the cheapest closed model at equal capability; break-even volume and how far hardware cost must fall. Plus posted GPU-hour prices and Hugging Face download counts.
无需输入参数。
Monthly measurement: dollars and tokens per completed task for the most-used models (fixed 8-task suite via OpenRouter at default settings, charged cost), reasoning share of output tokens, accuracy, and medians.
无需输入参数。
已满足适用检查项的 90%
该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。
请在仓库或注册中心条目中声明许可证。
curl -s https://infercpi.com/v1/latest.json | jq '.latest["weighted_index/paid_usd_1m"]'claude mcp add --transport http infercpi https://infercpi.com/mcp # Claude Codemkdir -p ~/.local/share/infercpicurl -so ~/.local/share/infercpi/mcp_server.py https://raw.githubusercontent.com/Infer-CPI/api/main/mcp_server.pypip install mcp # mcp 1.x or 2.x both workclaude mcp add infercpi -s user -- python3 ~/.local/share/infercpi/mcp_server.py # Claude Codepython3 ~/.local/share/infercpi/mcp_server.py --http # or: streamable HTTP on :8765{ "mcpServers": { "infercpi": { "command": "python3", "args": ["/home/YOU/.local/share/infercpi/mcp_server.py"] } } }