OptimToken

io.github.OptimNow由 GitHub 帳號 OptimNow 發布v0.4.2更新於 Oct 24, 2026

Compare LLM API and cloud compute pricing, estimate workload costs, pick a model within budget.

已驗證Streamable HTTP可網頁執行雲端與基礎設施金融AI 與機器學習

概覽

AI 產生的概覽

託管型 MCP 伺服器,讓助理查詢最新的 LLM API 與雲端運算價格、估算工作負載成本,並在預算內推薦模型。

可以做什麼
  • 依價格、品質(ELO)、效率與能力瀏覽並篩選 LLM 目錄
  • 在指定月用量下,將 2 至 4 個指定模型依 8 種使用情境設定檔逐一比較
  • 依自行提供的 token 數量、快取命中率與批次適用性估算單次請求與每月成本
  • 針對單一工作負載,在預算、最低 ELO、所需能力等限制下給出排名前三的建議
  • 依區域與類別比較 AWS、Azure、GCP、DigitalOcean、OCI、OVH 與阿里雲的 Linux 隨需執行個體價格
適用情境
當你需要附日期與來源的價格、而非模型憑記憶給出的數字時適用:例如估算 LLM 工作負載預算、在幾個指定模型間取捨,或挑選雲端執行個體。所有工具皆為唯讀查詢,適合規劃與比較用途。
執行需求
需要遠端 streamable HTTP 端點,無需安裝。請依原樣加入託管網址,包含 /mcp 路徑且結尾不加斜線,因為互動式小工具僅對此形式呈現。不需要任何憑證或環境變數。
安裝前請注意
價格來自第三方目錄;主要資料來源無法連線時會退回 OpenRouter 或內嵌快照,較低層級提供的是未經校正的價格,使用前請查看 provenance 欄位與提示訊息。內嵌備援資料為手動更新,可能過時。查詢內容會傳送至供應商伺服器。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 OptimToken,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.OptimNow%2Fai-pricing-hub/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "optimtoken": {
      "type": "http",
      "url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}

README

OptimToken MCP

Built by OptimNow. Ask an AI assistant what a model or an instance actually costs, and get a dated, sourced figure instead of a number the model remembers from its training data.

[CI] [MCP Server] [ChatGPT Apps] [Node] [Prices] [License: MIT] [GitHub Stars]


Connect in 30 seconds

The server is hosted, so there is nothing to install.

https://optimtoken-mcp.optimnow.io/mcp

Paste it exactly as written, with /mcp and no trailing slash. The widgets render only for that form: the host checks a hash of the URL you entered.

ClientHow to add it
[Claude Code]claude mcp add --transport http optimtoken https://optimtoken-mcp.optimnow.io/mcp
[Claude.ai / Desktop]Settings → Connectors → Add custom connector, paste the URL above
[ChatGPT]Settings → Connectors → Add, paste the URL. Comparisons render as interactive widgets
[Cursor] [Windsurf] [VS Code]Add an HTTP MCP server entry pointing at the URL

Then just ask:

"We send 200k support tickets a month at about 1,500 input tokens each. Which model gives me the best quality per euro, and what would it cost?"


Why this exists

Model prices change weekly, and a language model's idea of them is frozen at its training cutoff. Ask one what Claude or GPT costs and you get a confident answer that was true some months ago, with no date attached and no way to tell. The same applies to cloud instance rates, which additionally vary by region in ways nobody memorises.

This server replaces recall with a lookup:

  • Live prices, not remembered ones. Every LLM figure comes from the OptimToken catalogue, which tracks 250+ models and refreshes daily.
  • Corrected prices. OptimToken keeps a committed price archive, a verified overrides table checked against vendor pricing pages, and an anomaly check that alarms on the half-price and double-price breaks upstream feeds occasionally publish. This server asks that catalogue rather than re-deriving prices itself.
  • Cost per request, not cost per million tokens. Price-per-token comparisons hide the thing you actually pay for. The tools apply your token shape, your cache hit rate and batch eligibility, and return a figure per request and per month.
  • Every answer carries its provenance. Which tier served it, and whether the prices were verified.

Tools

ToolWhat it answers
compare-llm-models"What is out there?" Browse and filter the catalogue on price, quality (Chatbot Arena ELO), efficiency and capabilities, with a self-hostability read from the licence.
recommend-llm-model"Just tell me which one." A ranked top 3 for one workload under your constraints (budget, minimum ELO, required capability, self-hostability), each with a per-constraint satisfied or violated breakdown as the evidence. Over-constrained queries return the nearest misses, labelled as such.
compare-models-side-by-side"How do these specific ones compare?" 2 to 4 named models across all 8 use case profiles at a chosen monthly volume, list and optimized cost for each.
estimate-llm-cost"What will this cost us per month?" Per-request and monthly cost for your own volume, token shape, cache hit rate and batch eligibility.
compare-compute-pricing"What should we run it on?" Compute instance rates across AWS, Azure, GCP, OCI, OVH, DigitalOcean and Alibaba, by region and category.

All five are read-only and take no credentials. Nothing you send is stored.

Use case profiles ship with realistic token shapes, so you do not have to invent them: Support Ticket, Knowledge Q&A, Meeting Summary, Marketing Content, Coding Task, Invoice Processing, Call Summary, Agent Workflow.


Where the numbers come from

optimtoken.optimnow.io is the single source of truth. When it cannot be reached, the server degrades in tiers rather than failing, and says which tier it used.

ToolTier 1Tier 2Tier 3
LLM toolsGET /api/llm-modelsOpenRouter directembedded snapshot
Compute toolGET /api/pricing?region=not availableembedded snapshot (137 rows)

Tiers 2 and 3 serve uncorrected prices, and that matters more than it sounds. An upstream feed once published a frontier model at half its real list price, which halves every monthly figure derived from it. So every response carries a provenance object with pricesVerified, and the lower tiers put a notice at the top of the answer. A fallback should never quietly downgrade correctness.

Tier 1 is accepted only when the catalogue reports that it is itself serving fresh upstream data. If the site is on its own fallback, it carries no corrections, and this server treats it accordingly.


Local development

Requires Node.js 24+.

bash
npm installnpm run dev              # Skybridge dev server + MCP inspector at localhost:3000npm test                 # schema conformance, serialisation precision, data sourcesnpm run build            # widgets + server

The static fallback catalogue is refreshed by hand, not on a schedule:

bash
npm run refresh-fallback

Because it is manual, check its dataAsOf before trusting a tier-3 response. An unrefreshed fallback ages silently.

text
ai-pricing-hub-mcp/├─ server/src/index.ts              # tool + widget registrations├─ server/src/lib/optimtoken-api.ts # the one base URL constant, fetch and timeout discipline├─ server/src/lib/                  # ranking, efficiency scoring, provenance, normalisation├─ server/src/data/                 # static fallback pricing + region maps└─ web/src/widgets/                 # React widgets rendered in the client

Built with Skybridge, deployed on Fly.io.


The rest of the family

OptimTokenThe web app. Same catalogue, full UI, an AI advisor and a public JSON API.
AI ROI CalculatorDoes the AI business case pay for itself. Same prices, plus harness costs and value modelling.
cloud-finops-skillsFinOps knowledge for AI agents: AWS, Azure, GCP, AI inference, SaaS.
finops-mcp-resourcesMCP servers, tutorials and client guides for cloud cost work.

License

Released under the MIT License.

Prices served by this server come from third-party sources and are provided as is, without warranty. Verify against vendor pricing pages before committing spend.


Questions about your own AI or cloud bill? Talk to OptimNow.

來源:README.md,提交 84296b5

工具

5

compare-llm-models

Compare LLM Models

Browse and filter the whole LLM catalogue and get back a ranked table: price, quality (ELO), efficiency and capabilities. Use this when the user wants to SEE THE FIELD — 'show me models under $1/1M', 'which providers have vision models', 'list open-weight models above ELO 1300'. For a single PICK under a budget use recommend-llm-model; to weigh 2-4 NAMED models against each other use compare-models-side-by-side. Prices come from optimtoken.optimnow.io where reachable; the response's `provenance` says which tier served them and whether they are vendor-verified. Filter by provider, price tier (category), openness, capability, price range, or minimum ELO score. Optionally enrich with business metrics for a use case. Price tier and openness are independent: a model can be Frontier-priced and open-weight at once. Reports both list-price cost and the optimized cost achievable with prompt caching and the batch API. Prices, costs and scores are final as returned: rounded and ranked server-side, and dated by `provenance`. The text block is a numbered table of the same figures.

輸入參數limitminElocategoryopennessprovidercapabilityvolumePresetmaxInputPriceuseCasePresetmaxOutputPrice

estimate-llm-cost

Estimate LLM Cost

Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. Cost figures are final as returned: rounded server-side and dated by `provenance`, with the lever behind each saving, or the reason there is none, named in the text.

輸入參數modelNamemonthlyVolumeuseCasePresetcustomInputTokenscustomOutputTokens

compare-models-side-by-side

Compare Models Side by Side

Compare 2-4 named LLM models against all 8 use-case profiles at a chosen monthly volume, showing list and optimized cost for each. Use when the user names specific models to weigh against each other, rather than filtering the whole catalogue. If they also supply their own token counts, or a volume outside 10k/100k/1m, use estimate-llm-cost instead. Every name is resolved against the catalogue and the outcome is reported in `resolution` and in the text: a name that matched nothing, matched several models, or duplicated an earlier pick is stated explicitly, and such a name gets no column rather than a zero cost. Prices and costs are final as returned: rounded server-side and dated by `provenance`.

輸入參數models必填volumePreset

recommend-llm-model

Recommend an LLM Model

Pick a model. Returns a ranked top 3 for one workload under optional constraints, each with a per-constraint satisfied/violated breakdown as the evidence. Use this when the user wants an ANSWER rather than a table — 'what should I use for support tickets under $500 a month'. To browse or filter the whole catalogue instead, use compare-llm-models. Constraints: (monthly budget, minimum ELO, required capability, self-hostability). Returns a top 3 as structured facts — efficiency rank, ELO, list and optimized cost, FinOps flag, volatility, and a per-constraint satisfied/violated breakdown. When nothing satisfies every constraint the query is reported as over-constrained, `recommendations` is empty, and the nearest misses are returned in `nearMisses` instead, each carrying the constraint it failed and flagged as a miss in the text. The ranking is computed server-side and is final.

輸入參數minEloopennessvolumePresetuseCasePreset必填maxMonthlyBudgetrequiredCapability

compare-compute-pricing

Compare Cloud Compute Pricing

Compare cloud compute instance pricing across AWS, Azure, GCP, DigitalOcean, OCI, OVH, and Alibaba. Filter by region, provider, vCPUs, memory, category, processor, or use case. All prices are Linux on-demand list prices in USD. Not every price column is live: `provenance.priceTypes` says which of each provider's price columns come from a live API, which are static constants, and which are unavailable, and `provenance.staticPriceColumns` lists the non-live ones outright; a savings-plan or reserved rate that appears there is a static estimate, not a live quote. Prices are returned as the catalogue serves them and dated by `provenance`.

輸入參數limitregionsortByuseCasecategorymaxVCPUsminVCPUsprovidermaxMemoryminMemoryprocessor

版本歷史

4
  1. v0.4.2最新Oct 8, 2026
  2. v0.4.1Oct 7, 2026
  3. v0.4.0Oct 5, 2026
  4. v0.2.0Sep 16, 2026

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