LLM Reference
Big Bench AudioactiveAudio

Big Bench Audio

Metric: Accuracy (%) (higher is better)Introduced: 2025

About

Speech reasoning benchmark testing whether a voice model understands and reasons over what is said, not just transcribes it. Covers 12 tasks including emotion recognition, spoken arithmetic, factual Q&A, and instruction following. Evaluated by Artificial Analysis on speech-to-speech models. Higher is better.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.

How to read this benchmark

This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.

Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.

Trust when:

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • Model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Don't trust when:

  • There is only one benchmark snapshot or the dataset appears stale.
  • Benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

Current modeled score band for tracked entries is roughly 97.6 – 29.2.

Window

May 24 to Jun 7

last 3 snapshots

Mean delta

-20.13

score points

Coverage

4

models in latest snapshot