The Aggregate — LLM benchmark aggregate

Top of the cross-benchmark aggregate ranking: every model placed on one Elo scale by an IRT model fit over public benchmark leaderboards (call about_the_aggregate for the current coverage counts). One row per model by default, fused across reasoning-effort settings. Supports paging via limit/offset.

{
  "type": "object",
  "properties": {
    "limit": {
      "type": "number",
      "description": "Rows to return (1-100, default 25)."
    },
    "offset": {
      "type": "number",
      "description": "Rows to skip from the top (default 0)."
    },
    "include_variants": {
      "type": "boolean",
      "description": "Rank each reasoning-effort variant separately (e.g. \"Claude Opus 4.6 (High)\") instead of one fused row per model. Default false."
    }
  }
}

Find ranked models by (partial) name or provider. Returns rank, Elo and the model page URL. One row per model by default, fused across reasoning-effort settings.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Model or provider name fragment, e.g. \"opus\" or \"deepseek\"."
    },
    "limit": {
      "type": "number",
      "description": "Max results (1-25, default 10)."
    },
    "include_variants": {
      "type": "boolean",
      "description": "Return each reasoning-effort variant separately (e.g. \"Claude Opus 4.6 (High)\") instead of one fused row per model. Default false."
    }
  },
  "required": [
    "query"
  ]
}

One model in depth: aggregate rank, Elo with standard error, provider, what it is, cost per task where known, and its most notable benchmark results (with percentiles).

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name or slug, e.g. \"Claude Opus 4.5\" or \"gpt-5-5\"."
    }
  },
  "required": [
    "model"
  ]
}

Head-to-head between 2-4 models: aggregate ranks, Elo gap with a significance note based on the standard errors, and notable benchmarks they share.

{
  "type": "object",
  "properties": {
    "models": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 2,
      "maxItems": 4,
      "description": "Two to four model names or slugs."
    }
  },
  "required": [
    "models"
  ]
}

Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the Elo scale, and the benchmark page URL.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Benchmark name fragment, e.g. \"swe-bench\" or \"arena\"."
    },
    "limit": {
      "type": "number",
      "description": "Max results (1-25, default 10)."
    }
  },
  "required": [
    "query"
  ]
}

One benchmark in depth: what it measures, the original source leaderboard URL, IRT stats (difficulty, noise, model coverage), skill weights, and the current top models on it.

{
  "type": "object",
  "properties": {
    "benchmark": {
      "type": "string",
      "description": "Benchmark name or slug, e.g. \"Aider polyglot\"."
    },
    "top": {
      "type": "number",
      "description": "How many top models to list (1-50, default 10)."
    }
  },
  "required": [
    "benchmark"
  ]
}

Guesswork — the public prediction duel: every day frontier LLMs and The Aggregate's own IRT model predict newly scraped benchmark scores before seeing them, and the errors are scored. Returns the current monthly standings, wins and losses included.

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

What this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.

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