Deepgram Flux English vs Deepgram Flux Multilingual
Deepgram Flux English is ahead of Deepgram Flux Multilingual on all four metrics.
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
- Deepgram Flux Multilingual
- 4.96%14th
Deepgram Flux English, 1.06 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Deepgram Flux English
- 4.18%7th
- Deepgram Flux Multilingual
- 4.53%13th
Deepgram Flux English, 0.35 points lower
Median delay before the first words arrive, from the first audio packet.
- Deepgram Flux English
- 791ms2nd
- Deepgram Flux Multilingual
- 793ms3rd
Deepgram Flux English, 2ms sooner
Median delay after the speaker stops before the transcript is final.
- Deepgram Flux English
- 108ms3rd
- Deepgram Flux Multilingual
- 114ms4th
Deepgram Flux English, 6ms 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 |
| Deepgram Flux Multilingual | 793ms | 2.00s | 3.41s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
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 English | 999 | 8 | 199 | 7 |
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
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 Deepgram Flux Multilingual more accurate?
On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and Deepgram Flux Multilingual 4.96%, so Deepgram Flux English makes fewer errors by 1.06 points. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and Deepgram Flux Multilingual 4.53%, so Deepgram Flux English makes fewer errors by 0.35 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 Deepgram Flux Multilingual?
Deepgram Flux English returns first text 2ms sooner, at 791ms against 793ms at the median. Deepgram Flux English has the final transcript 6ms sooner, 108ms after the speaker stops against 114ms. Both are measured on the same 206 streamed turns.
How were Deepgram Flux English and Deepgram Flux Multilingual 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 Deepgram Flux Multilingual for 999. Latency uses each provider's own supported finalization contract.