Results
Deepgram Nova 3 accuracy and speed
Deepgram Nova 3 is a hosted API aimed at real-time and high-volume transcription. It is quick and it holds up well on clean read speech.
75
Blended score
10.1%
Word error rate
0.50 s
Wait for the text
Macro average over 8 datasets
| Dataset | Score ↑ | WER ↓ | Speed ↑ | Recall ↑ | F-score ↑ | Response ↓ |
|---|---|---|---|---|---|---|
| AMI Meetings20260731-174311 | 72 | 11.3% | 32× | — | — | 0.50 s |
| Common Voice, Australian Englishexternal-cv24-australia-500-20260801 | 88 | 4.7% | 15× | — | — | 0.50 s |
| Common Voice, spontaneous Englishexternal-cv-spontaneous4-test-20260801 | 76 | 9.7% | 34× | — | — | 0.50 s |
| Earnings 2220260731-174311 | 75 | 9.8% | 19× | — | — | 0.50 s |
| Earnings 22 (with vocabulary)20260731-175836 | 53 | 18.9% | 35× | 89.4% | 92.1% | 0.50 s |
| LibriSpeech Other20260731-174311 | 88 | 4.8% | 29× | — | — | 0.50 s |
| Loquaciousexternal-loquacious-500-20260801 | 78 | 9.0% | 20× | — | — | 0.50 s |
| Phonetic vocabulary20260731-175836 | 68 | 12.7% | 4.4× | 77.1% | 80.6% | 0.50 s |
Cloud results do not depend on the machine that made the request, so they are not split by device.
Updated August 27, 2026 from bench commit 5344cab. The second line of each row is the run that produced it.
Method
How this was measured
Cloud results come from calling the model over its public API with the same audio as everything else in the benchmark.
Word error rate counts insertions, deletions and substitutions against a human transcript, so lower is better. Speed is a multiple of real time, so higher is better. The full method and every other model is on the benchmarks page.
Compare
Other models in the benchmark
Same audio, same machines, same method.
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