MCP server
Model Tested Official
com.modeltested/llm-benchmarks
Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools
Independent test scores for open-weight LLMs: best model for your GPU or Mac, comparisons.
Working at last checkVerified publisher: modeltested.comAbout 936 tokens per request once connected5 tools
https://modeltested.com/mcp
At a glance
- Type
- MCP server
- Publisher
- modeltested.com (verified by the MCP Registry)
- Status
- Working at last check
- Tools
- 5
- Token cost
- About 936 tokens per request
- Sets up in
- Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
- Last checked
- 26 Oct 2026, 08:29 UTC
Connect it to your agent
Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.
Claude Code
claude mcp add --transport http llm-benchmarks https://modeltested.com/mcp
Claude Desktop
Settings → Connectors → Add custom connector, and paste the URL.
Codex (config.toml)
[mcp_servers.llm-benchmarks] url = "https://modeltested.com/mcp"
Gemini CLI
{"mcpServers":{"llm-benchmarks":{"httpUrl":"https://modeltested.com/mcp"}}}
Cursor
{"mcpServers":{"llm-benchmarks":{"url":"https://modeltested.com/mcp"}}}
VS Code
{"servers":{"llm-benchmarks":{"type":"http","url":"https://modeltested.com/mcp"}}}
Antigravity
agy mcp add llm-benchmarks https://modeltested.com/mcp
ChatGPT
Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.
OpenAI API (Responses tools)
{"type":"mcp","server_label":"llm-benchmarks","server_url":"https://modeltested.com/mcp","require_approval":"always"}
Grok (xAI API tools)
{"type":"mcp","server_label":"llm-benchmarks","server_url":"https://modeltested.com/mcp"}
Tools
compare_models~117 tokens
Use this whenever the user asks how two or more open-weight models compare, or which of them to pick. Returns our test scores, rank and memory needs for 2 to 6 models side by side.decision_models~206 tokens
Use this whenever the user asks about decision models or LLMs for classification, routing, triage or yes/no calls. Accuracy on our decision test (routing tickets, applying rules, spotting phishing, flagging risky code changes), how well each model's confidence matches its accuracy (calibration), andfind_models~311 tokens
Use this whenever the user asks which local or open-weight LLM is best, overall or for coding, documents or decisions, or which model they can run on their graphics card (VRAM) or Mac. Returns tested models ranked by our own test scores, best first; with a GPU or Mac memory size it only returns modeget_model~167 tokens
Use this whenever the user asks about a specific open-weight model: how good it is, its benchmark scores, whether it's worth using, how much VRAM or memory it needs, or which quantization to download. Returns our test scores, rank, memory at each quantization, the smallest GPU and Mac it fits, decissearch_articles~135 tokens
Find Model Tested articles and ranking pages by keyword, newest first: news about newly tested models, guides on quantization, Ollama and memory. Returns title, summary, date and link.
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