ComparEdge LLM Cost

io.github.imkemit-ops由 GitHub 账号 imkemit-ops 发布v1.0.0更新于 Oct 25, 2026

Token cost math for LLM API calls: verified per-1M-token rates for 69 models, 17 providers.

发布方自己的服务：Edge Network

已验证STDIO仅桌面金融 AI 与机器学习

服务详情

标识符: io.github.imkemit-ops/comparedge-llm-cost
传输: STDIO
运行时: Desktop only
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 imkemit-ops。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

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概览

AI 生成的概览

一个本地 MCP 服务器，为助手提供 69 个模型的每百万 token 已验证费率，并计算单次调用、工作负载和预算成本。

可以做什么: 按模型和 token 数量估算单次调用或多次相同调用的美元成本，并在模型支持时给出缓存与批量价格。将同一次调用在 2 到 6 个模型间比价，按最便宜排序。根据调用量和平均 token 数推算每日、每月和每年支出。查找成本最低的模型（可设上下文长度下限），并列出已收录的模型与提供商及其费率和上下文。
适用场景: 适合在规划或说明 AI 功能规模化后的成本、按价格挑选模型、或预测每月 LLM 账单时使用，并希望数字来自带日期的价格源，而不是模型凭记忆给出的价格。
运行要求: 通过 stdio 在本地运行，通常用 npx 从 npm 包启动，因此需要 Node.js。无需 API 密钥或账号。它会联网获取一个公开价格源并缓存约六小时，获取失败时使用上一份可用副本。

安装前请注意

费用数字是基于第三方价格源和你提供的 token 数得出的估算，并非账单记录，实际扣费可能不同。服务器不发送任何凭据，也不调用提供商 API，但会发起网络请求获取公开价格源。

安装

在 SourceWeft 中

打开控制台中的 ComparEdge LLM Cost，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照仓库中的启动说明。

README

[llm-cost: token cost math for LLM API calls]

[models] [providers] [dependencies] [license]

Someone asks what the AI feature will cost at scale, and the honest answer around most teams is a shrug. Rates moved twice since anyone last checked, and the model itself will happily quote prices from its training data. This MCP server keeps current per-million-token rates for 69 models where your assistant can reach them, and does the arithmetic itself.

Watch it work

$ You: price Claude Opus 4.8 on a 25k-token prompt with a 1k answer, run 5,000 times
  llm-cost › estimate_cost
  Cost estimate: Claude Opus 4.8 (Anthropic)  Rates: input $5/1M, output $25/1M, cached input $0.5/1M
  Per call (25,000 in + 1,000 out tokens):    input:  $0.1250    output: $0.0250    per call total: $0.1500
  Across 5,000 calls: $750.00
  Ways to pay less for the same 5,000 calls:    with cached input:  $187.50    via batch API:      $375.00

$ You: price Claude Opus 4.8 on a 25k-token prompt with a 1k answer, run 5,000 times
  llm-cost › estimate_cost
  Cost estimate: Claude Opus 4.8 (Anthropic)  Rates: input $5/1M, output $25/1M, cached input $0.5/1M
  Per call (25,000 in + 1,000 out tokens):    input:  $0.1250    output: $0.0250    per call total: $0.1500
  Across 5,000 calls: $750.00
  Ways to pay less for the same 5,000 calls:    with cached input:  $187.50    via batch API:      $375.00

Nothing here is rounded or guessed. The rate is verified, date-stamped, and the server multiplied.

Open a second session: same call across four models, ranked

$ You: compare that call on Opus 4.8, Sonnet 5, GPT-5.6 Terra and Gemini 3.1 Pro
  llm-cost › compare_models_cost
  25,000 in + 1,000 out, cheapest first:
  1. Claude Sonnet 5      $0.0600 /call    $300.00 /5k  2. Gemini 3.1 Pro       $0.0620 /call    $310.00 /5k  3. GPT-5.6 Terra        $0.0775 /call    $387.50 /5k  4. Claude Opus 4.8      $0.1500 /call    $750.00 /5k
  Ranking uses each model's live feed entry, not remembered prices.

$ You: compare that call on Opus 4.8, Sonnet 5, GPT-5.6 Terra and Gemini 3.1 Pro
  llm-cost › compare_models_cost
  25,000 in + 1,000 out, cheapest first:
  1. Claude Sonnet 5      $0.0600 /call    $300.00 /5k  2. Gemini 3.1 Pro       $0.0620 /call    $310.00 /5k  3. GPT-5.6 Terra        $0.0775 /call    $387.50 /5k  4. Claude Opus 4.8      $0.1500 /call    $750.00 /5k
  Ranking uses each model's live feed entry, not remembered prices.

The gap it closes

An assistant on its own	An assistant with llm-cost
quotes output rates from training data, often a generation stale	reads the current rate, dated
flattens the estimate to "a few cents"	`$0.1500` per call, `$750.00` across 5,000
never mentions batch or caching discounts	`$375.00` batch, `$187.50` cached, only where the model really offers them
confidently wrong, no way to tell	every figure traces to a feed entry with a verification date

The same numbers answer in a browser through the LLM calculator, and the LLM category hub ranks every tracked model by rating and price.

Where the numbers travel

mermaid

flowchart LR    V["provider pricing pages<br/>17 providers"] --> CE["verification<br/>date-stamped checks"]    CE --> F["model prices feed<br/>69 models, USD per 1M tokens"]    F -->|"6h cache, serve stale on failure"| MCP["llm-cost server<br/>local arithmetic"]    MCP --> A["your agent"]

flowchart LR    V["provider pricing pages<br/>17 providers"] --> CE["verification<br/>date-stamped checks"]    CE --> F["model prices feed<br/>69 models, USD per 1M tokens"]    F -->|"6h cache, serve stale on failure"| MCP["llm-cost server<br/>local arithmetic"]    MCP --> A["your agent"]

The server never calls a provider API. It reads one public feed, llms-model-prices.json, and computes locally. Nothing to rate-limit, no key to leak, and a network hiccup serves the last good copy instead of an error. How each price gets checked is written up in the methodology, and the catalog behind it ships as an open dataset under CC BY 4.0.

The six tools

Four do the math. Two help you find the exact model id the math wants.

Tool	Answers	Params
`estimate_cost`	one call, or N identical calls, in dollars	`model`, `input_tokens`, `output_tokens`, `calls?`
`compare_models_cost`	the same call priced across 2 to 6 models	`models[]`, `input_tokens`, `output_tokens`
`monthly_budget`	daily, monthly, yearly spend for a workload	`model`, `daily_calls`, `avg_input_tokens`, `avg_output_tokens`
`cheapest_models`	lowest-cost models, optional context floor	`min_context?`, `limit?`
`list_models`	every model with rates, context and tier	`provider?`
`list_providers`	providers with model counts and cheapest pick	none

Token rules of thumb, for when nobody knows the counts

A page of English prose is roughly 500 tokens; one token is about four characters.
Model references are forgiving: claude-opus-4-8, Opus 4.8 and anthropic/opus resolve to the same model. When the resolver is unsure, it returns candidates instead of guessing.
cheapest_models ranks by a blended rate weighting input to output 3 to 1, because real workloads read far more than they write. Confirm the winner with estimate_cost on your actual split.

Prompts

Prompt	Args	Runs
`estimate_my_workflow`	`workflow`, `model?`	token estimates plus per-run and monthly cost for a described workflow
`pick_cheapest_model`	`task`	cheapest model that still meets the requirement, top candidates priced
`forecast_ai_budget`	`model`, `usage`	monthly and yearly bill projected from expected volume

Wire it up

json

{  "mcpServers": {    "llm-cost": {      "command": "npx",      "args": ["-y", "@comparedge/llm-cost-mcp@latest"]    }  }}

{  "mcpServers": {    "llm-cost": {      "command": "npx",      "args": ["-y", "@comparedge/llm-cost-mcp@latest"]    }  }}

Claude Desktop keeps this file at ~/Library/Application Support/Claude/claude_desktop_config.json. Cursor: Settings, then MCP. VS Code with Copilot reads .vscode/mcp.json. Restart the client; six tools appear. No API key, no account. Per-client walkthroughs live in the setup guide.

Family

Built by ComparEdge, where software prices are checked against vendor pages before anyone quotes them. Two siblings share the data: the full catalog server and a price-change watcher, both on ComparEdge MCP.

MIT licensed. JSON-RPC 2.0 over stdio, standard Model Context Protocol.

来源：README.md，提交 c413e54