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Données ouvertes

Bibliothèque

Jeux de données ouverts, entièrement documentés — interrogeables ici, et lisibles par n’importe quel LLM.

Les titres et les descriptions proviennent des sources de données, en anglais.

2 jeux de données

LLM benchmark scoreboard (agent-curated)

  • LLM benchmark scoreboard (agent-curated)

    LLM composite capability index (monthly)

    Monthly per-model composite of the cross-leaderboard scoreboard: for every model appearing in LMArena's official public leaderboard (text/overall, CC-BY-4.0) or Epoch AI's Capabilities & Benchmarking hub (CC-BY), the composite index is the equal-weighted mean of its normalized 0-100 score components (LMArena Bradley-Terry rating rescaled + each Epoch benchmark rescaled), with n_components recorded so single-signal models are distinguishable from broadly measured ones. Epoch's own Capabilities Index (ECI) is carried as a reference column, never a composite input. Columns: snapshot date, composite rank, resolved model key, display name, resolution tier, organization, ISO alpha-3 country, accessibility, component count, Epoch benchmarks covered, LMArena variants merged, Epoch mean normalized, LMArena normalized, LMArena rating/rank/votes, Epoch ECI + CI, composite index, source URLs. Primary key: (snapshot_date, model_key). Cadence: monthly. Caveats: a transparency-first average, not a capability claim; benchmarks differ in difficulty and LMArena measures human preference. Sample use: order by composite_rank for the current cross-source model ranking, or filter n_components >= 5 for broadly-measured models only.

    • ai-research
    • machine-learning
    • technology
    • signals
    lignes
    546
    Qualité
    96
    Mis à jour
    25 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • LLM benchmark scoreboard (agent-curated)

    LLM benchmark scores (monthly)

    Monthly cross-leaderboard scoreboard of large language models: every (model, benchmark) score from LMArena's official public leaderboard dataset (Bradley-Terry ratings, text/overall arena, CC-BY-4.0) and Epoch AI's Capabilities & Benchmarking data hub (56 current capability benchmarks, CC-BY), rescaled per benchmark to a comparable 0-100 (min-max within the snapshot; all benchmarks are higher-is-better) with per-benchmark ranks. Models are entity-resolved across the two sources (exact name match, then one arena-variant suffix stripped, unambiguous only; tier recorded per row). Superseded Epoch benchmarks are pruned. Columns: snapshot date (day-granular UTC), source, benchmark, benchmark release date, resolved model key, raw upstream model id, display name, resolution tier, organization, ISO alpha-3 country, raw score, score unit, normalized 0-100 score, rank in benchmark, models in benchmark, vote count (LMArena), source publish date, source URL. Primary key: (snapshot_date, source, benchmark, source_model_id). Cadence: monthly; same-day re-runs are content-hash no-ops. Caveats: the 0-100 scale is within-benchmark relative, not absolute; LMArena measures human preference, Epoch benchmarks measure task accuracy — the composite dataset blends them explicitly. Sample use: filter benchmark = 'GPQA diamond' order by rank_in_benchmark for the current reasoning-benchmark ranking.

    • ai-research
    • machine-learning
    • technology
    • signals
    lignes
    3 121
    Qualité
    99
    Mis à jour
    25 sept. 2026
    À jour
    Licence
    Usage commercial OK

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