GPT Realtime Whisper vs Smallest Pulse
Smallest Pulse is ahead on the Ocular Dataset, time to first text and final-text delay, and GPT Realtime Whisper on the Pipecat Dataset.
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
The four metrics, side by side
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset.
- GPT Realtime Whisper
- 2.16%4th
- Smallest Pulse
- 3.66%11th
GPT Realtime Whisper, 1.50 points lower
Word error rate on eight licensed recordings provided by Ocular.
- GPT Realtime Whisper
- 3.53%4th
- Smallest Pulse
- 3.46%3rd
Smallest Pulse, 0.07 points lower
Median delay before the first words arrive, from the first audio packet.
- GPT Realtime Whisper
- 1.38s8th
- Smallest Pulse
- 1.06s4th
Smallest Pulse, 322ms sooner
Median delay after the speaker stops before the transcript is final.
- GPT Realtime Whisper
- 543ms12th
- Smallest Pulse
- 201ms8th
Smallest Pulse, 342ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field.
First text and final text, at three percentiles
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 | GPT Realtime Whisper | 1.38s | 1.84s | 2.86s |
| Smallest Pulse | 1.06s | 2.25s | 3.03s | |
| Final-text delay | GPT Realtime Whisper | 543ms | 647ms | 669ms |
| Smallest Pulse | 201ms | 356ms | 773ms |
Both against every other model
Open on the metric where the two sit furthest apart, with the other three a tab away.
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset. Lower is better.
| # | Model | Pipecat Dataset | clips | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 1.93% | 1,000 | |
| 2 | Google Chirp 3 | 2.00% | 1,000 | |
| 3 | Reson8 | 2.10% | 1,000 | |
| 4 | GPT Realtime Whisper | 2.16% | 1,000 | |
| 5 | Cartesia Ink 2 | 2.39% | 1,000 | |
| 6 | Speechmatics Linden | 2.46% | 998 | |
| 7 | Google Chirp 2 | 2.59% | 1,000 | |
| 8 | Inworld STT-1 | 2.72% | 1,000 | |
| 9 | GPT-4o Mini Transcribe | 3.35% | 1,000 | |
| 10 | Deepgram Nova-3 | 3.46% | 1,000 | |
| 11 | Smallest Pulse | 3.66% | 1,000 | |
| 12 | GPT-4o Transcribe | 3.90% | 1,000 | |
| 13 | Deepgram Flux English | 3.90% | 999 | |
| 14 | Deepgram Flux Multilingual | 4.96% | 999 | |
| 15 | Gradium | 6.48% | 1,000 |
Counts are the clips each model returned usable text for, so they differ between models and between datasets.
Both on the other two views
Pipecat vs Ocular accuracy
Every model's error rate on the public clips, against the same model on real conversations.
Transcription response time
How soon each model returns its first words, against how soon its transcript is final.
What the numbers rest on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| GPT Realtime Whisper | 1,000 | 8 | 206 | 0 |
| Smallest Pulse | 1,000 | 8 | 206 | 0 |
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.
Frequently asked questions
Is GPT Realtime Whisper or Smallest Pulse more accurate?
On the 1,000 public Pipecat clips, GPT Realtime Whisper scores 2.16% and Smallest Pulse 3.66%, so GPT Realtime Whisper makes fewer errors by 1.50 points. On the eight licensed Ocular recordings, GPT Realtime Whisper scores 3.53% and Smallest Pulse 3.46%, so Smallest Pulse makes fewer errors by 0.07 points. The two datasets disagree, so the better choice depends on whether your audio looks more like clean public clips or real conversations.
Which is faster, GPT Realtime Whisper or Smallest Pulse?
Smallest Pulse returns first text 322ms sooner, at 1.06s against 1.38s at the median. Smallest Pulse has the final transcript 342ms sooner, 201ms after the speaker stops against 543ms. Both are measured on the same 206 streamed turns.
How were GPT Realtime Whisper and Smallest Pulse compared?
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. GPT Realtime Whisper returned usable text for 1,000 of the public clips and Smallest Pulse for 1,000. Latency uses each provider's own supported finalization contract.