HELM Audio - Fairness: leaderboard
The "Fairness" subtask of HELM Audio. Stanford HELM's audio project (AHELM): audio-language models are given speech and sound clips and scored on transcription, spoken-question answering, reasoning over audio, speaker and emotion attributes, safety and fairness.
Metric: Mean win rate. Source: crfm.stanford.edu. Saturation forecast: Estimated already saturated. 17 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 2.5 Pro (Preview 05-06) | 90.62 | #148 |
| 2 | Gemini 2.0 Flash | 75 | #344 |
| 3 | Gemini 2.0 Flash (Preview) | 71.88 | #366 |
| 4 | Gemini 1.5 Pro (002) | 68.75 | #350 |
| 5 | Gemini 1.5 Flash (002) | 53.12 | #585 |
| 6 | Gemini 2.0 Flash Lite | 53.12 | #453 |
| 7 | Gemini 1.5 Pro (001) | 31.25 | #446 |
| 8 | Gemini 2.5 Flash (Preview 05-20) | 25 | #214 |
| 9 | Qwen2.5-Omni-7B | 21.88 | #632 |
| 10 | Qwen2-Audio-7B-Instruct | 9.38 | #888 |
| 11 | Gemini 1.5 Flash (001) | 3.12 | #580 |
No result here: #4 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=helm-audio-fairness · How It Works · Data refreshed daily, snapshot 2026-10-23.