Deepgram Flux English vs Reson8
Deepgram Flux English is ahead on time to first text and final-text delay, and Reson8 on the Pipecat Dataset and the Ocular 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.
- Deepgram Flux English
- 3.90%13th
- Reson8
- 2.10%3rd
Reson8, 1.81 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Deepgram Flux English
- 4.18%7th
- Reson8
- 2.93%1st
Reson8, 1.25 points lower
Median delay before the first words arrive, from the first audio packet.
- Deepgram Flux English
- 791ms2nd
- Reson8
- 2.40s11th
Deepgram Flux English, 1.61s sooner
Median delay after the speaker stops before the transcript is final.
- Deepgram Flux English
- 108ms3rd
- Reson8
- 235ms10th
Deepgram Flux English, 127ms 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 | Deepgram Flux English | 791ms | 1.23s | 1.51s |
| Reson8 | 2.40s | 7.97s | 10.20s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Reson8 | 235ms | 357ms | 398ms |
Both against every other model
Open on the metric where the two sit furthest apart, with the other three a tab away.
Time to first text, measured from the first audio packet. Lower is better.
| # | Model | First text | turns | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 489ms | 205 | |
| 2 | Deepgram Flux English | 791ms | 199 | |
| 3 | Deepgram Flux Multilingual | 793ms | 175 | |
| 4 | Smallest Pulse | 1057ms | 206 | |
| 5 | Inworld STT-1 | 1063ms | 206 | |
| 6 | Deepgram Nova-3 | 1064ms | 203 | |
| 7 | Speechmatics Linden | 1190ms | 194 | |
| 8 | GPT Realtime Whisper | 1379ms | 206 | |
| 9 | Cartesia Ink 2 | 1511ms | 198 | |
| 10 | Gradium | 1705ms | 198 | |
| 11 | Reson8 | 2400ms | 201 | |
| 12 | Google Chirp 3 | 4480ms | 206 | |
| 13 | Google Chirp 2 | 4497ms | 205 |
Not ranked: GPT-4o Mini Transcribe and GPT-4o Transcribe return no text until the speaker stops, so first-text time is not comparable.
Median across the same 206 Ocular Dataset turns. Providers finalize differently, so compare models with similar finalization contracts before treating small gaps as meaningful.
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 |
|---|---|---|---|---|
| Deepgram Flux English | 999 | 8 | 199 | 7 |
| Reson8 | 1,000 | 8 | 201 | 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 Deepgram Flux English or Reson8 more accurate?
On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and Reson8 2.10%, so Reson8 makes fewer errors by 1.81 points. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and Reson8 2.93%, so Reson8 makes fewer errors by 1.25 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Deepgram Flux English or Reson8?
Deepgram Flux English returns first text 1.61s sooner, at 791ms against 2.40s at the median. Deepgram Flux English has the final transcript 127ms sooner, 108ms after the speaker stops against 235ms. Both are measured on the same 206 streamed turns.
How were Deepgram Flux English and Reson8 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. Deepgram Flux English returned usable text for 999 of the public clips and Reson8 for 1,000. Latency uses each provider's own supported finalization contract.