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model-price-radar-mcp

What does each LLM cost today, and did it get cheaper? An MCP server over OpenRouter's public model catalog (~450 models) plus monthly price history since 2023, rebuilt from Wayback Machine copies of the catalog. Ask Claude (or any MCP client) for the cheapest model that can do a job, what a workload will cost, or how a model's price moved.

No API keys. One line to install.

You:    Did LLM prices really go down? Show me GPT-4o and DeepSeek Chat.
Claude: [model_price_history ×2, price_changes]
        • GPT-4o: $5 / $15 per 1M tokens until Oct 2024, $2.50 / $10 since Nov 2024.
        • DeepSeek Chat changed price 12 times since mid-2024; input went from
          $0.14 to $0.26 per 1M.
        • Not everything gets cheaper: since Oct 2025 older open models went up
          as cheap hosts dropped them, e.g. Qwen 2.5 Coder 32B input $0.04 → $0.66.

Summarized from real tool output, 1 Oct 2026.

DeepSeek Chat price history by model-price-radar

Why

You want to know Without it With model-price-radar
Cheapest model with tools + vision + 200k context Scroll pricing pages find_models(needs=["tools","vision"], min_context=200000)
What 100k calls/month will cost on 5 models Spreadsheet estimate_cost ranks them with a "× cheapest" column
Did this model's price change? Nobody keeps history model_price_history since the model appeared
What moved in LLM pricing this quarter Twitter price_changes lists drops, increases, newly free models
Which provider serves Llama cheapest Compare tabs compare_providers with quantization and uptime

How it works

flowchart LR
    C[Claude / MCP client] -->|tool call| S[model-price-radar-mcp]
    S --> L[OpenRouter /api/v1/models: live prices, context, capabilities]
    S --> E[OpenRouter /models/id/endpoints: per-provider prices]
    S --> H[Bundled monthly history 2023→build date]
    S --> W[Wayback Machine: months archived after the build]
    L & H & W --> C

Price history ships inside the package (18 KB, one snapshot per month since July 2023), so history answers are instant; months archived after the release are fetched from the Wayback Machine on demand and cached. Prices are US$ per 1M tokens; "blended" assumes 3 input tokens per output token.

Install

Requires uv.

Claude Code

claude mcp add model-price-radar -- uvx model-price-radar-mcp

Claude Desktop / Cursor (claude_desktop_config.json / .cursor/mcp.json)

{
  "mcpServers": {
    "model-price-radar": {
      "command": "uvx",
      "args": ["model-price-radar-mcp"]
    }
  }
}

Tools

Tool What it does
find_models Search the live catalog by name, capabilities (tools, vision, reasoning, structured outputs, audio, free), price ceilings, context
estimate_cost Cost of a workload (tokens per call × calls) on up to 10 models, cheapest first
model_price_history Every price change of one model since it appeared, with % changes
price_changes Since a month: biggest drops and increases, newly free models, models added/removed
compare_providers Same model across providers: price, context, max output, quantization, uptime

Model names are forgiving: "claude sonnet" resolves to the newest Sonnet, "llama 3.1 70b" to meta-llama/llama-3.1-70b-instruct.

Prompts: cheapest_model_for (a task → recommended model with monthly cost), monthly_price_report.

Try these

  • "Cheapest model with tool calling and vision for 50k support tickets a day? Show monthly cost."
  • "How did Claude and GPT prices change since 2024?"
  • "What got cheaper in LLM pricing since January?"
  • "Which provider serves Llama 4 Maverick cheapest, and at what quantization?"

Limits

  • Prices are OpenRouter's, which usually equal each provider's list price. Batch and enterprise discounts are not included.
  • History has one point per archived month, so a change is dated "between these two months".
  • Models are tracked by OpenRouter id; a renamed id starts a new history.

Development

uv sync --extra dev
uv run pytest                              # offline tests with mocked OpenRouter + Wayback
uv run python scripts/smoke_live.py        # live check
uv run python scripts/build_history.py     # rebuild the bundled history (several minutes)
uv run --with rich python scripts/demo.py deepseek/deepseek-chat   # terminal demo (vhs docs/demo.tape records the GIF)

MIT © Ali Altunar

Metadata

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