WavBench - Explicit Acoustic Understanding: leaderboard
Metric: Accuracy (%) of the model's predictions against ground-truth labels when explicitly asked to identify a paralinguistic attribute of the spoken input, unweighted mean over ten attributes (accent, age, emotion, gender, language, pitch, speed, volume, audio event, music); higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 5 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen3 Omni 30B A3B Instruct | 49.6 | #362 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.
Interactive version: theaggregate.ai/benchmark?slug=wavbench-explicit-acoustic-understanding · How It Works · Data refreshed daily, snapshot 2026-10-11.