LibriSpeech WER: LibriSpeech WER (test-clean)
About
Word Error Rate on the LibriSpeech test-clean benchmark, using read English speech from audiobooks. The industry's long-standing clean-speech baseline for ASR; top models now reach sub-2% WER. Lower 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 lower 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 1.3 – 1.3.
Window
Mar 7 to Apr 30
last 3 snapshots
Mean delta
+0.08
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. Granite Speech 4.1 2B1.3
- 2. Transcribe (03-2026)1.3