LLM Benchmark MCP Server

MCP server that gives AI agents access to LLM benchmark data, pricing comparisons, and model recommendations.

Features

• compare_models — Side-by-side benchmark comparison of LLMs (MMLU, HumanEval, MATH, GPQA, ARC, HellaSwag)

• get_model_details — Detailed info about a specific model including strengths/weaknesses

• recommend_model — Get the best model recommendation for your task and budget

• list_top_models — Top models ranked by category (coding, math, reasoning, chat)

• get_pricing — Pricing comparison via OpenRouter API

Related MCP server: Artificial Analysis MCP Server

Supported Models

GPT-4o, GPT-4o-mini, GPT-4 Turbo, o1, o3-mini, Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus, Gemini 2.0 Flash, Gemini 2.0 Pro, Gemini 1.5 Pro, Llama 3.1 (8B/70B/405B), Llama 3.3 70B, Mistral Large, Mistral Small, Mixtral 8x22B, DeepSeek V3, DeepSeek R1, Qwen 2.5 72B

Installation

pip install llm-benchmark-mcp-server

Usage with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "llm-benchmark": {
      "command": "benchmark-server"
    }
  }
}

Or via uvx (no install needed):

{
  "mcpServers": {
    "llm-benchmark": {
      "command": "uvx",
      "args": ["llm-benchmark-mcp-server"]
    }
  }
}

Example Queries

• "Compare GPT-4o vs Claude 3.5 Sonnet vs Gemini 2.0 Pro"

• "Which model is best for coding on a low budget?"

• "Show me the top 10 models for math"

• "What does GPT-4o cost compared to Claude?"

• "Give me details about DeepSeek R1"

Data Sources

• Benchmarks: Hardcoded from official papers and public leaderboards (MMLU, HumanEval, MATH, GPQA, ARC-Challenge, HellaSwag)

• Pricing: Live data from OpenRouter API

• Arena Rankings: Chatbot Arena Leaderboard (when available)

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License

MIT