Model Tested

com.modeltested/llm-benchmarksVersion 1.0.0

Independent test scores for open-weight LLMs: best model for your GPU or Mac, comparisons.

Status

Responds144 msNov 14, 2026, 14:06 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:06 UTC
Latency: 144 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 21:12 UTC

Data from the official MCP Registry, checked by run.zero. How we check

Connect

Streamable HTTP

https://modeltested.com/mcpResponds144 msNov 14, 2026, 14:06 UTC

No sign-in

Claude Code

claude mcp add --transport http llm-benchmarks https://modeltested.com/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: llm-benchmarks
Remote MCP server URL: https://modeltested.com/mcp

Cursor

{
  "mcpServers": {
    "llm-benchmarks": {
      "url": "https://modeltested.com/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"llm-benchmarks","type":"http","url":"https://modeltested.com/mcp"}'

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Tools

5 tools

Tools of Model Tested
	Description		Read-only	Idempotent	Open world
`compare_models`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.	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.	Not declared	Yes	Not declared	No
`decision_models`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), and API cost per 1,000 calls. Covers dedicated decision models and regular LLMs given the same tasks.	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), and API cost per 1,000 calls. Covers dedicated decision models and regular LLMs given the same tasks.	Not declared	Yes	Not declared	No
`find_models`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 models that fit, plus the quantization to download and the memory it needs. Examples: 'best model for a 16 GB GPU', 'best coding model on a 32 GB Mac', 'best small model under 10B'.	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 models that fit, plus the quantization to download and the memory it needs. Examples: 'best model for a 16 GB GPU', 'best coding model on a 32 GB Mac', 'best small model under 10B'.	Not declared	Yes	Not declared	No
`get_model`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, decision accuracy and our Q4 run on a Mac when there is one. Accepts a name or slug, e.g. 'qwen3.5 9b' or 'gpt-oss-20b'.	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, decision accuracy and our Q4 run on a Mac when there is one. Accepts a name or slug, e.g. 'qwen3.5 9b' or 'gpt-oss-20b'.	Not declared	Yes	Not declared	No
`search_articles`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.	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.	Not declared	Yes	Not declared	No

Details

Registry status: Active
Published: Oct 18, 2026
Updated: Oct 18, 2026
Website: https://modeltested.com/mcp/⁠ (opens in a new tab)
Repository: https://github.com/jelly-ham/modeltested⁠ (opens in a new tab)
Categories (suggested): AI modelsSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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