BenchLeader

Daily LLM leaderboard from independent benchmarks: ranks, scores, prices, speed and comparisons.

使うべきか

品質と安全性

A
説明の品質
100%
スキーマの完全性
87%
命名の品質
94%
ポイズニングのリスク
100%
権限の一致
100%
プロトコルへの準拠
100%

ツール定義とプロトコルへの準拠に関する自動分析に基づいています。

コンテキストコスト

~1,463トークン数(ツール定義)
~1.4 KB一般的なレスポンスサイズ
注意への影響は中程度(128k コンテキストの 1.14%)

これは、サーバーのツールがモデルのコンテキストに読み込まれるたびに消費されるおおよそのトークン数です。数が多いほど、ほかのタスクに使える注意が減ります。

インストール

ワンクリックインストール

これを `claude_desktop_config.json` ファイルに追加してください:

{
  "mcpServers": {
    "benchleader": {
      "url": "https://www.benchleader.com/mcp"
    }
  }
}

リモートエンドポイント

https://www.benchleader.com/mcpstreamable-http

できること

ツール一覧

ツール(7)

🟢 読み取り専用🟡 書き込み🔴 削除⚪ 不明
🟢get_leaderboard(category, configurations, open_weights_only, developer, limit, ...)

The BenchLeader Index leaderboard of large language models: a daily composite of independent public benchmarks, one row per model (its best reasoning-effort configuration) as on the homepage. Optionally rank by one category score (coding, agentic, reasoning, math, knowledge…), list every model × effort configuration, or filter to open-weights models or one developer. Paged.

入力スキーマ

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Rank by this category score instead of the overall Index, e.g. coding, agentic, reasoning, math, knowledge, instruction, multimodal, long_context."
    },
    "configurations": {
      "type": "boolean",
      "default": false,
      "description": "List every ranked model × reasoning-effort configuration rather than one row per model."
    },
    "open_weights_only": {
      "type": "boolean",
      "default": false,
      "description": "Only models with downloadable weights."
    },
    "developer": {
      "type": "string",
      "description": "Only models from this developer, e.g. OpenAI, Anthropic, Google, DeepSeek."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "default": 20,
      "description": "How many rows to return (at most 100)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "default": 0,
      "description": "Rows to skip, for paging (use next_offset from the previous page)."
    }
  },
  "additionalProperties": false
}
🟢search_models(query, limit)

Find models by name, alias, id or developer ("opus", "gemini 4", "deepseek"). Returns each match's id, rank, Index and reasoning-effort configurations, for use with get_model and compare_models.

入力スキーマ

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Model name, alias, id or developer."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 25,
      "default": 10,
      "description": "How many matches to return (at most 25)."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}
🟢get_model(model, effort, results_limit)

One model's BenchLeader profile: Index and rank, category scores, list price, speed, latency, context window, its other reasoning-effort configurations, and its strongest independent benchmark results (each with its board rank, publisher and source link).

入力スキーマ

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model id (\"claude-opus-5-5\"), configuration id (\"gpt-6-astra@max\"), page URL or name."
    },
    "effort": {
      "type": "string",
      "description": "Reasoning-effort configuration to show (none, low, medium, high, xhigh, max…). Defaults to the best-ranked one."
    },
    "results_limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 50,
      "default": 10,
      "description": "How many benchmark results to return (at most 50)."
    }
  },
  "required": [
    "model"
  ],
  "additionalProperties": false
}
🟢compare_models(model_a, model_b, effort_a, effort_b)

Head-to-head comparison of two models with BenchLeader's verdict: who leads on quality (a lead is claimed only outside the combined margin of error), price and speed ratios, category-by-category wins, when to choose each, and shared benchmark results. Links the comparison page.

入力スキーマ

{
  "type": "object",
  "properties": {
    "model_a": {
      "type": "string",
      "description": "First model: id, configuration id, page URL or name."
    },
    "model_b": {
      "type": "string",
      "description": "Second model: id, configuration id, page URL or name."
    },
    "effort_a": {
      "type": "string",
      "description": "Reasoning effort for model_a (defaults to its best-ranked configuration)."
    },
    "effort_b": {
      "type": "string",
      "description": "Reasoning effort for model_b (defaults to its best-ranked configuration)."
    }
  },
  "required": [
    "model_a",
    "model_b"
  ],
  "additionalProperties": false
}
🟢list_benchmarks(category, query, in_index_only, limit, offset)

The benchmarks BenchLeader reads: what each measures, publisher, category, whether it feeds the Index (and its weight), how many models it covers, status and caveats. Filter by category or a search query. Paged.

入力スキーマ

{
  "type": "object",
  "properties": {
    "category": {
      "type": "string",
      "description": "Only this category, e.g. coding, agentic, reasoning, math, knowledge."
    },
    "query": {
      "type": "string",
      "description": "Search benchmark names, ids, aliases and publishers (\"swe-bench\", \"arc\", \"epoch\")."
    },
    "in_index_only": {
      "type": "boolean",
      "default": false,
      "description": "Only benchmarks that feed the BenchLeader Index."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "default": 50,
      "description": "How many benchmarks to return (at most 100)."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "default": 0,
      "description": "Rows to skip, for paging (use next_offset from the previous page)."
    }
  },
  "additionalProperties": false
}
🟢get_benchmark(benchmark, limit)

One benchmark: what it measures, unit, publisher and source, whether and how much it counts in the BenchLeader Index, status and caveats, and its top models (one row per model, best result first).

入力スキーマ

{
  "type": "object",
  "properties": {
    "benchmark": {
      "type": "string",
      "description": "Benchmark id (\"gpqa_diamond\"), name (\"SWE-bench Verified\"), alias or page URL."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 25,
      "default": 10,
      "description": "How many top models to return (at most 25)."
    }
  },
  "required": [
    "benchmark"
  ],
  "additionalProperties": false
}
🟢about_benchleader

What BenchLeader is, how the BenchLeader Index is computed, the current method version, data date and update cadence, coverage counts, licence and how to cite it.

入力スキーマ

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

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