Deepgram Flux English vs Deepgram Flux Multilingual
Deepgram Flux English is ahead of Deepgram Flux Multilingual on the Pipecat Dataset, time to first text and final-text delay. The Ocular Dataset is 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 Deepgram Flux Multilingual on every measure that separates them: accuracy on clean, scripted audio (1.06 points lower), showing words while the caller is still talking (2ms sooner) and quick turn-taking once the caller stops (6ms sooner). For 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
- Deepgram Flux Multilingual
- 4.96%16th
Deepgram Flux English, 1.06 points lower
- Deepgram Flux English
- 4.18%8th
- Deepgram Flux Multilingual
- 4.53%15th
Too close to call: inside the margin of error
- Deepgram Flux English
- 791ms3rd
- Deepgram Flux Multilingual
- 793ms4th
Deepgram Flux English, 2ms sooner
- Deepgram Flux English
- 108ms5th
- Deepgram Flux Multilingual
- 114ms6th
Deepgram Flux English, 6ms 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 Deepgram Flux Multilingual shows first text after 3.41s and final text after 141ms, 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 |
| Deepgram Flux Multilingual | 793ms | 2.00s | 3.41s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
- 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 | Flux Multilingual |
|---|---|---|
| Pipecat Dataset WER | 3.90%15th of 17 | 4.96%16th of 17 |
| Ocular Dataset WER | 4.18%8th of 17 | 4.53%15th of 17 |
| Time to first text | 791ms3rd of 15 | 793ms4th of 15 |
| Final-text delay | 108ms5th of 17 | 114ms6th 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 |
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
- Deepgram Flux English vs GPT-4o Transcribe
- Deepgram Flux English vs Smallest Pulse
- Deepgram Flux English vs Deepgram Nova-3
- Deepgram Flux English vs GPT-4o Mini Transcribe
- Deepgram Flux English vs Inworld STT-1
- Deepgram Flux English vs Soniox STT-RT v5
- Deepgram Flux English vs Speechmatics Linden
- Deepgram Flux English vs Cartesia Ink 2
- Deepgram Flux English vs GPT Realtime Whisper
- 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
- Deepgram Flux Multilingual vs GPT-4o Transcribe
- Deepgram Flux Multilingual vs Smallest Pulse
- Deepgram Flux Multilingual vs Deepgram Nova-3
- Deepgram Flux Multilingual vs Gradium
- Deepgram Flux Multilingual vs GPT-4o Mini Transcribe
- Deepgram Flux Multilingual vs Inworld STT-1
- Deepgram Flux Multilingual vs Soniox STT-RT v5
- Deepgram Flux Multilingual vs Speechmatics Linden
- Deepgram Flux Multilingual vs Cartesia Ink 2
- Deepgram Flux Multilingual vs GPT Realtime Whisper
- Deepgram Flux Multilingual vs Reson8
- Deepgram Flux Multilingual vs Google Chirp 3
- Deepgram Flux Multilingual vs AssemblyAI Universal 3.6 Pro
Frequently asked questions
Which is more accurate, Deepgram Flux English or Deepgram Flux Multilingual?
Deepgram Flux English is more accurate on the public clips. 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%, with likely ranges of 3.08 to 5.23% and 3.29 to 5.69%. 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 Deepgram Flux Multilingual?
Yes. Deepgram Flux English finishes the transcript 6ms sooner. Deepgram Flux English has the final transcript 108ms after the speaker stops and Deepgram Flux Multilingual 114ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux English or Deepgram Flux Multilingual better for voice agents?
Deepgram Flux English finishes the transcript 6ms 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 Deepgram Flux Multilingual 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 Deepgram Flux Multilingual after 793ms, at the median, so Deepgram Flux English is 2ms sooner.