Deepgram Flux Multilingual vs Reson8
Deepgram Flux Multilingual 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 Multilingual
- 4.96%14th
- Reson8
- 2.10%3rd
Reson8, 2.86 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Deepgram Flux Multilingual
- 4.53%13th
- Reson8
- 2.93%1st
Reson8, 1.60 points lower
Median delay before the first words arrive, from the first audio packet.
- Deepgram Flux Multilingual
- 793ms3rd
- Reson8
- 2.40s11th
Deepgram Flux Multilingual, 1.61s sooner
Median delay after the speaker stops before the transcript is final.
- Deepgram Flux Multilingual
- 114ms4th
- Reson8
- 235ms10th
Deepgram Flux Multilingual, 121ms 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 Multilingual | 793ms | 2.00s | 3.41s |
| Reson8 | 2.40s | 7.97s | 10.20s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 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.
Word error rate on eight licensed recordings provided by Ocular. Lower is better.
| # | Model | Ocular Dataset | clips | |
|---|---|---|---|---|
| 1 | Reson8 | 2.93% | 8 | |
| 2 | Cartesia Ink 2 | 3.09% | 8 | |
| 3 | Smallest Pulse | 3.46% | 8 | |
| 4 | GPT Realtime Whisper | 3.53% | 8 | |
| 5 | Google Chirp 2 | 3.80% | 8 | |
| 6 | Speechmatics Linden | 3.93% | 8 | |
| 7 | Deepgram Flux English | 4.18% | 8 | |
| 8 | Google Chirp 3 | 4.19% | 8 | |
| 9 | AssemblyAI Universal 3.5 Pro | 4.23% | 8 | |
| 10 | Deepgram Nova-3 | 4.39% | 8 | |
| 11 | Gradium | 4.45% | 8 | |
| 12 | GPT-4o Mini Transcribe | 4.45% | 8 | |
| 13 | Deepgram Flux Multilingual | 4.53% | 8 | |
| 14 | Inworld STT-1 | 10.60% | 8 | |
| 15 | GPT-4o Transcribe | 12.45% | 8 |
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 |
|---|---|---|---|---|
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
| 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 Multilingual or Reson8 more accurate?
On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and Reson8 2.10%, so Reson8 makes fewer errors by 2.86 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and Reson8 2.93%, so Reson8 makes fewer errors by 1.60 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Deepgram Flux Multilingual or Reson8?
Deepgram Flux Multilingual returns first text 1.61s sooner, at 793ms against 2.40s at the median. Deepgram Flux Multilingual has the final transcript 121ms sooner, 114ms after the speaker stops against 235ms. Both are measured on the same 206 streamed turns.
How were Deepgram Flux Multilingual 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 Multilingual returned usable text for 999 of the public clips and Reson8 for 1,000. Latency uses each provider's own supported finalization contract.