Smallest Pulse is ahead on the Ocular Dataset and time to first text, and Soniox STT-RT v5 on the Pipecat Dataset and final-text delay.
Every model's error rate on the public clips, against how long its transcript takes to settle.
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset.
Soniox STT-RT v5, 1.11 points lower
Word error rate on eight licensed recordings provided by Ocular.
Smallest Pulse, 0.47 points lower
Median delay before the first words arrive, from the first audio packet.
Smallest Pulse, 138ms sooner
Median delay after the speaker stops before the transcript is final.
Soniox STT-RT v5, 163ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field.
Both measured on the same 206 streamed Ocular Dataset turns. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Smallest Pulse | 1.06s | 2.25s | 3.03s |
| Soniox STT-RT v5 | 1.20s | 1.67s | 1.71s | |
| Final-text delay | Smallest Pulse | 201ms | 356ms | 773ms |
| Soniox STT-RT v5 | 38ms | 72ms | 79ms |
Open on the metric where the two sit furthest apart, with the other three a tab away.
Final-text delay after the speaker stops. Lower is better.
| # | Model | Final text | turns | |
|---|---|---|---|---|
| 1 | Soniox STT-RT v5 | 38ms | 200 | |
| 2 | Inworld STT-1 | 41ms | 206 | |
| 3 | AssemblyAI Universal 3.6 Pro | 91ms | 206 | |
| 4 | Deepgram Nova-3 | 103ms | 202 | |
| 5 | Deepgram Flux English | 108ms | 199 | |
| 6 | Deepgram Flux Multilingual | 114ms | 175 | |
| 7 | Cartesia Ink 2 | 123ms | 198 | |
| 8 | Speechmatics Linden | 147ms | 194 | |
| 9 | AssemblyAI Universal 3.5 Pro | 180ms | 205 | |
| 10 | Smallest Pulse | 201ms | 206 | |
| 11 | Gradium | 212ms | 198 | |
| 12 | Reson8 | 235ms | 201 | |
| 13 | Google Chirp 3 | 475ms | 206 | |
| 14 | GPT Realtime Whisper | 543ms | 206 | |
| 15 | Google Chirp 2 | 568ms | 205 | |
| 16 | GPT-4o Mini Transcribe | 630ms | 206 | |
| 17 | GPT-4o Transcribe | 652ms | 206 |
Median across the same 206 Ocular Dataset turns. Providers finalize differently, so compare models with similar finalization contracts before treating small gaps as meaningful.
Every model's error rate on the public clips, against the same model on real conversations.
How soon each model returns its first words, against how soon its transcript is final.
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Smallest Pulse | 1,000 | 8 | 206 | 0 |
| Soniox STT-RT v5 | 1,000 | 8 | 200 | 6 |
A clip counts only where the model returned usable text, so counts differ between models. Word error rate is measured the same way throughout: incorrect, missing, and extra words against the reference transcript.
On the 1,000 public Pipecat clips, Smallest Pulse scores 3.66% and Soniox STT-RT v5 2.54%, so Soniox STT-RT v5 makes fewer errors by 1.11 points. On the eight licensed Ocular recordings, Smallest Pulse scores 3.46% and Soniox STT-RT v5 3.92%, so Smallest Pulse makes fewer errors by 0.47 points. The two datasets disagree, so the better choice depends on whether your audio looks more like clean public clips or real conversations.
Smallest Pulse returns first text 138ms sooner, at 1.06s against 1.20s at the median. Soniox STT-RT v5 has the final transcript 163ms sooner, 38ms after the speaker stops against 201ms. Both are measured on the same 206 streamed turns.
Both ran the same two datasets under the same conditions as every other model in the benchmark: 1,000 public clips from Pipecat's STT benchmark dataset and eight licensed conversation recordings from Ocular. Smallest Pulse returned usable text for 1,000 of the public clips and Soniox STT-RT v5 for 1,000. Latency uses each provider's own supported finalization contract.