Model Tested
Independent test scores for open-weight LLMs: best model for your GPU or Mac, comparisons.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"llm-benchmarks": {
"url": "https://modeltested.com/mcp"
}
}
}Remote endpoints
https://modeltested.com/mcpstreamable-httpWhat it can do
Tool inventory
Tools (5)
🟢find_models(sort_by, hardware, memory_gb, max_params_b, limit)
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'.
Input Schema
{
"type": "object",
"properties": {
"sort_by": {
"type": "string",
"enum": [
"overall",
"coding",
"documents",
"decisions",
"hard"
],
"description": "Score to rank by. Default overall."
},
"hardware": {
"type": "string",
"enum": [
"gpu",
"mac"
],
"description": "Graphics card (VRAM) or Mac (unified memory)."
},
"memory_gb": {
"type": "number",
"description": "VRAM or Mac memory in GB. Needs hardware."
},
"max_params_b": {
"type": "number",
"description": "Only models with at most this many billion parameters."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 30,
"description": "Default 10."
}
}
}🟢get_model(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'.
Input Schema
{
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "Model name or slug."
}
},
"required": [
"model"
]
}🟢compare_models(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.
Input Schema
{
"type": "object",
"properties": {
"models": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 2,
"maxItems": 6,
"description": "Model names or slugs."
}
},
"required": [
"models"
]
}🟢decision_models(type, limit)
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.
Input Schema
{
"type": "object",
"properties": {
"type": {
"type": "string",
"enum": [
"decision",
"llm",
"all"
],
"description": "decision = dedicated decision models only (default), llm = regular LLMs only."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 50,
"description": "Default 15."
}
}
}🟢search_articles(query, limit)
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.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Words to look for, e.g. 'quantization' or 'ornith'. Empty lists the latest."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 20,
"description": "Default 8."
}
}
}Community
Evidence