Deepgram Flux Multilingual vs Deepgram Nova-3
Deepgram Nova-3 is ahead on the Pipecat Dataset, the Ocular Dataset and final-text delay, and Deepgram Flux Multilingual on time to first text.
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
- Deepgram Nova-3
- 3.46%10th
Deepgram Nova-3, 1.50 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Deepgram Flux Multilingual
- 4.53%13th
- Deepgram Nova-3
- 4.39%10th
Deepgram Nova-3, 0.14 points lower
Median delay before the first words arrive, from the first audio packet.
- Deepgram Flux Multilingual
- 793ms3rd
- Deepgram Nova-3
- 1.06s6th
Deepgram Flux Multilingual, 271ms sooner
Median delay after the speaker stops before the transcript is final.
- Deepgram Flux Multilingual
- 114ms4th
- Deepgram Nova-3
- 103ms2nd
Deepgram Nova-3, 11ms 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 |
| Deepgram Nova-3 | 1.06s | 1.10s | 2.07s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
| Deepgram Nova-3 | 103ms | 143ms | 155ms |
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 |
|---|---|---|---|---|
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
| Deepgram Nova-3 | 1,000 | 8 | 202 | 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 Deepgram Nova-3 more accurate?
On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and Deepgram Nova-3 3.46%, so Deepgram Nova-3 makes fewer errors by 1.50 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and Deepgram Nova-3 4.39%, so Deepgram Nova-3 makes fewer errors by 0.14 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 Deepgram Nova-3?
Deepgram Flux Multilingual returns first text 271ms sooner, at 793ms against 1.06s at the median. Deepgram Nova-3 has the final transcript 11ms sooner, 103ms after the speaker stops against 114ms. Both are measured on the same 206 streamed turns.
How were Deepgram Flux Multilingual and Deepgram Nova-3 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 Deepgram Nova-3 for 1,000. Latency uses each provider's own supported finalization contract.