AI Model Leaderboards

Where does the leaderboard data come from?

Scores are aggregated from primary sources: official model cards, technical reports, papers, vendor blog posts, and reproducible third-party evaluations. Each row links back to the underlying model detail page where the source is cited.

Why do scores for the same model differ across benchmarks?

Each benchmark measures a different capability — reasoning (HLE, ARC-AGI-2), math (AIME, FrontierMath), coding (SWE-bench Verified), agent use (τ²-Bench), and so on. A model tuned for one capability may perform very differently on another, which is exactly why we surface per-benchmark scores rather than a single number.

How often is the leaderboard updated?

Data is revalidated every 5 minutes, and new models or evaluation results are added as soon as they are published. The "Updated on" indicator at the top of the page reflects the most recent data refresh.

How should I read the composite ranking?

The composite view aggregates a model's standing across multiple core benchmarks. It is a useful first filter, but for production decisions you should drill into the specific benchmark closest to your workload — for example, SWE-bench Verified for coding agents, or τ²-Bench for tool-use scenarios.

How do I compare an open-source model with a closed API model?

Use the license filter at the top to mix open and closed models in the same view, then look at the same benchmark column for both. Beyond raw scores, consider total cost of ownership: API pricing for closed models vs. self-hosting cost for open weights.