Deepgram Flux English vs Smallest Pulse
Deepgram Flux English is ahead of Smallest Pulse on time to first text and final-text delay. The Pipecat Dataset and The Ocular Dataset are too close to call.
Last updated Methodology by Dileep Chagam
Accuracy vs speed
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
Which to pick
Deepgram Flux English is the better pick over Smallest Pulse on every measure that separates them: showing words while the caller is still talking (266ms sooner) and quick turn-taking once the caller stops (93ms sooner). For accuracy on clean, scripted audio and accuracy on real phone conversations, the gap is inside the margin of error, so neither is ahead.
The four metrics, side by side
- Deepgram Flux English
- 3.90%15th
- Smallest Pulse
- 3.66%13th
Too close to call: inside the margin of error
- Deepgram Flux English
- 4.18%8th
- Smallest Pulse
- 3.46%3rd
Too close to call: inside the margin of error
- Deepgram Flux English
- 791ms3rd
- Smallest Pulse
- 1.06s5th
Deepgram Flux English, 266ms sooner
- Deepgram Flux English
- 108ms5th
- Smallest Pulse
- 201ms10th
Deepgram Flux English, 93ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field. What each metric measures.
- WER · Word error rate
- Share of words the model got wrong: replaced, missing or added. Lower is better.
- Pipecat Dataset
- 1,000 short public clips from Pipecat's STT benchmark dataset.
- Ocular Dataset
- 8 recordings from 4 real phone conversations, licensed from Ocular.
- TTFT · Time to first text
- How long after the audio starts streaming until the first words appear.
- TTFS · Final-text delay
- How long after the caller stops until the full transcript arrives. A voice agent waits for this before it replies.
First text and final text, at three percentiles
Deepgram Flux English shows first text after 1.51s and final text after 141ms, and Smallest Pulse shows first text after 3.03s and final text after 773ms, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Deepgram Flux English | 791ms | 1.23s | 1.51s |
| Smallest Pulse | 1.06s | 2.25s | 3.03s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Smallest Pulse | 201ms | 356ms | 773ms |
- P50 / P90 / P95 · Typical, slow and slowest turns
- Half of turns are faster than P50. Only 1 in 10 is slower than P90, and 1 in 20 slower than P95.
How each ranks among all 17 models
Place among the 17 models on each metric. Lower is better on all four.
| Metric | Flux English | Pulse |
|---|---|---|
| Pipecat Dataset WER | 3.90%15th of 17 | 3.66%13th of 17 |
| Ocular Dataset WER | 4.18%8th of 17 | 3.46%3rd of 17 |
| Time to first text | 791ms3rd of 15 | 1.06s5th of 15 |
| Final-text delay | 108ms5th of 17 | 201ms10th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux English | 999 | 8 | 199 | 7 |
| Smallest Pulse | 1,000 | 8 | 206 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
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- Deepgram Flux English vs GPT-4o Mini Transcribe
- Deepgram Flux English vs Deepgram Flux Multilingual
- Deepgram Flux English vs Inworld STT-1
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- Deepgram Flux English vs Reson8
- Deepgram Flux English vs Google Chirp 3
- Deepgram Flux English vs AssemblyAI Universal 3.6 Pro
- Deepgram Flux English vs Gradium
- Smallest Pulse vs Deepgram Nova-3
- Smallest Pulse vs GPT-4o Transcribe
- Smallest Pulse vs GPT-4o Mini Transcribe
- Smallest Pulse vs Inworld STT-1
- Smallest Pulse vs Soniox STT-RT v5
- Smallest Pulse vs Speechmatics Linden
- Smallest Pulse vs Cartesia Ink 2
- Smallest Pulse vs Deepgram Flux Multilingual
- Smallest Pulse vs GPT Realtime Whisper
- Smallest Pulse vs Reson8
- Smallest Pulse vs Google Chirp 3
- Smallest Pulse vs AssemblyAI Universal 3.6 Pro
- Smallest Pulse vs Gradium
Frequently asked questions
Which is more accurate, Deepgram Flux English or Smallest Pulse?
Deepgram Flux English and Smallest Pulse are too close to call on accuracy. On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and Smallest Pulse 3.66%, with likely ranges of 3.53 to 4.29% and 3.34 to 3.99%. The ranges overlap, so the difference is too close to call. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and Smallest Pulse 3.46%, with likely ranges of 3.08 to 5.23% and 2.66 to 4.22%. The ranges overlap, so the difference is too close to call. Word error rate counts incorrect, missing, and extra words, so lower is better.
Is Deepgram Flux English faster than Smallest Pulse?
Yes. Deepgram Flux English finishes the transcript 93ms sooner. Deepgram Flux English has the final transcript 108ms after the speaker stops and Smallest Pulse 201ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux English or Smallest Pulse better for voice agents?
Deepgram Flux English finishes the transcript 93ms sooner, and the two are too close to call on real conversations. A voice agent cannot reply until the final transcript arrives, and the Ocular recordings are real conversations.
Do Deepgram Flux English and Smallest Pulse stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux English shows first text after 791ms and Smallest Pulse after 1.06s, at the median, so Deepgram Flux English is 266ms sooner.