Updated daily at 05:00 UTC
Benchmarks
Speech & audio
Transcribing speech accurately, and understanding or reasoning over spoken audio, sound and music; voice quality is ranked under human preference.
How the score works
A model's score is built only from results that someone other than its maker ran.
- The scoring set is the benchmarks chosen for this specialty. A benchmark takes part only when at least 5 models have an independent or third-party result on it.
- Each result is rescaled to 0 to 1, and a model's best setting counts.
- The score is the average over the benchmarks a model has, times 100.
- A model ranks only when it has results on enough of them. The rest show "Not enough independent results yet".
- Self-reported results are shown but never counted.
Method: category-2026-09-29.1
Scoring set and effective set
Scoring set: Artificial Analysis Speech to Text (AA-WER), Open ASR Leaderboard, τ-Voice (tau-Voice).
Effective set (used for the score today): none yet.
- Artificial Analysis Speech to Text (AA-WER): no importable independent results yet
- Open ASR Leaderboard: no importable independent results yet
- τ-Voice (tau-Voice): no importable independent results yet
All benchmarks
- Artificial Analysis Speech to Text (AA-WER)ActiveNear-ceilingCounts toward the score0 results
- Big Bench AudioActiveSaturated0 results
- MMAU-ProActiveHeadroom0 results
- Open ASR LeaderboardActiveNear-ceilingCounts toward the score0 results
- τ-Voice (tau-Voice)ActiveHeadroomCounts toward the score0 results