Model intelligence should be explainable.
Artificial Intelligence DataBase is designed as an evidence-driven reference layer for people choosing between fast-moving AI models.
What we measure
We organize model performance into separate dimensions such as reasoning, coding, knowledge, instruction following, multimodal capability, speed, cost efficiency and context handling.
What we show
We present three distinct signals: benchmark evidence, expert/internal evaluation and community experience. A community star rating is never disguised as a verified benchmark score.
How rankings work
Ranking profiles use explicit, versioned weights and evidence-aware confidence. Each leaderboard exposes its profile, formula version and calculation date.
Read the methodology →Sample data policy
The current MVP uses clearly labelled sample data to exercise the product. Sample scores are illustrative and must not be interpreted as real-world model performance.
Independence and trust
Future sponsored placements, provider-submitted evidence and editorial content should remain visibly separated from official ranking calculations.
Future direction
The architecture is intentionally ready for persistent storage, verified expert contributions, automated benchmark runs, pairwise evaluation and additional locales.