Deepgram Flux Multilingual vs GPT Realtime Whisper
Deepgram Flux Multilingual is ahead on time to first text and final-text delay, and GPT Realtime Whisper on the Pipecat Dataset. 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
Pick Deepgram Flux Multilingual for showing words while the caller is still talking (586ms sooner) and quick turn-taking once the caller stops (429ms sooner). Pick GPT Realtime Whisper for accuracy on clean, scripted audio (2.80 points lower). 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 Multilingual
- 4.96%16th
- GPT Realtime Whisper
- 2.16%5th
GPT Realtime Whisper, 2.80 points lower
- Deepgram Flux Multilingual
- 4.53%15th
- GPT Realtime Whisper
- 3.53%4th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 793ms4th
- GPT Realtime Whisper
- 1.38s10th
Deepgram Flux Multilingual, 586ms sooner
- Deepgram Flux Multilingual
- 114ms6th
- GPT Realtime Whisper
- 543ms14th
Deepgram Flux Multilingual, 429ms 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 Multilingual shows first text after 3.41s and final text after 141ms, and GPT Realtime Whisper shows first text after 2.86s and final text after 669ms, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Deepgram Flux Multilingual | 793ms | 2.00s | 3.41s |
| GPT Realtime Whisper | 1.38s | 1.84s | 2.86s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
| GPT Realtime Whisper | 543ms | 647ms | 669ms |
- 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 Multilingual | Realtime Whisper |
|---|---|---|
| Pipecat Dataset WER | 4.96%16th of 17 | 2.16%5th of 17 |
| Ocular Dataset WER | 4.53%15th of 17 | 3.53%4th of 17 |
| Time to first text | 793ms4th of 15 | 1.38s10th of 15 |
| Final-text delay | 114ms6th of 17 | 543ms14th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
| GPT Realtime Whisper | 1,000 | 8 | 206 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
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Frequently asked questions
Which is more accurate, Deepgram Flux Multilingual or GPT Realtime Whisper?
GPT Realtime Whisper is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and GPT Realtime Whisper 2.16%, so GPT Realtime Whisper makes fewer errors by 2.80 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and GPT Realtime Whisper 3.53%, with likely ranges of 3.29 to 5.69% and 2.58 to 4.51%. 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 Multilingual faster than GPT Realtime Whisper?
Yes. Deepgram Flux Multilingual finishes the transcript 429ms sooner. Deepgram Flux Multilingual has the final transcript 114ms after the speaker stops and GPT Realtime Whisper 543ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux Multilingual or GPT Realtime Whisper better for voice agents?
Deepgram Flux Multilingual finishes the transcript 429ms 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 Multilingual and GPT Realtime Whisper stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux Multilingual shows first text after 793ms and GPT Realtime Whisper after 1.38s, at the median, so Deepgram Flux Multilingual is 586ms sooner.