AudioProcessBench - Best-of-N Selection: leaderboard

AudioProcessBench (2026) tests audio and omni-modal models as critics of audio-grounded reasoning: given an audio question and a model-written reasoning chain split into steps, the critic marks each step correct or wrong; 3,872 annotated chains from MMAR, MMSU and MMAU-Pro questions. This board uses the step judgments of the critic to pick the best of several chains for the same question and reports final-answer accuracy in percent; higher is better.

Metric: Final-answer accuracy (%) when the critic's summed step scores pick one chain among several sampled for the same audio question; 3,872 reasoning traces (23,497 steps, 41% erroneous) from six audio and omni models on MMAR, MMSU and MMAU-Pro questions, steps labelled by two LLM annotators with human review; few-shot critic prompt with three annotated example chains, temperature 0.7; random 47.5; higher is better. Source: arxiv.org. Saturation forecast: Around 2030. 11 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Flash59.4#164
2Phi-4 Multimodal Instruct52.9#3882
3Gemma 4 E2B52.5#3892
4Gemma 4 E4B51.6#2214
5Qwen2.5-Omni-7B51.1#1789
6Gemma 3n E4B50.8#3301
7Gemma 3n E2B50.3#6091

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=audioprocessbench-best-of-n-selection · How It Works · Data refreshed daily, snapshot 2026-11-13.