Deepgram Flux Multilingual vs GPT-4o Mini Transcribe
Deepgram Flux Multilingual is ahead on final-text delay, and GPT-4o Mini Transcribe 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 quick turn-taking once the caller stops (516ms sooner). Pick GPT-4o Mini Transcribe for accuracy on clean, scripted audio (1.61 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-4o Mini Transcribe
- 3.35%11th
GPT-4o Mini Transcribe, 1.61 points lower
- Deepgram Flux Multilingual
- 4.53%15th
- GPT-4o Mini Transcribe
- 4.45%12th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 793ms4th
- GPT-4o Mini Transcribe
- No text until the speaker stops
Not comparable
- Deepgram Flux Multilingual
- 114ms6th
- GPT-4o Mini Transcribe
- 630ms16th
Deepgram Flux Multilingual, 516ms 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-4o Mini Transcribe shows first text after 40.84s and final text after 1.53s, 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-4o Mini Transcribe | No text until the speaker stops | |||
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
| GPT-4o Mini Transcribe | 630ms | 1.34s | 1.53s | |
- 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 | 4o Mini Transcribe |
|---|---|---|
| Pipecat Dataset WER | 4.96%16th of 17 | 3.35%11th of 17 |
| Ocular Dataset WER | 4.53%15th of 17 | 4.45%12th of 17 |
| Time to first text | 793ms4th of 15 | Not comparable |
| Final-text delay | 114ms6th of 17 | 630ms16th 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-4o Mini Transcribe | 1,000 | 8 | 206 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
Both providers, every model: Deepgram vs OpenAI
- Deepgram Flux Multilingual vs Deepgram Flux English
- 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 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
- GPT-4o Mini Transcribe vs Deepgram Nova-3
- GPT-4o Mini Transcribe vs Smallest Pulse
- GPT-4o Mini Transcribe vs GPT-4o Transcribe
- GPT-4o Mini Transcribe vs Deepgram Flux English
- GPT-4o Mini Transcribe vs Inworld STT-1
- GPT-4o Mini Transcribe vs Soniox STT-RT v5
- GPT-4o Mini Transcribe vs Speechmatics Linden
- GPT-4o Mini Transcribe vs Cartesia Ink 2
- GPT-4o Mini Transcribe vs GPT Realtime Whisper
- GPT-4o Mini Transcribe vs Reson8
- GPT-4o Mini Transcribe vs Google Chirp 3
- GPT-4o Mini Transcribe vs AssemblyAI Universal 3.6 Pro
- GPT-4o Mini Transcribe vs Gradium
Frequently asked questions
Which is more accurate, Deepgram Flux Multilingual or GPT-4o Mini Transcribe?
GPT-4o Mini Transcribe is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and GPT-4o Mini Transcribe 3.35%, so GPT-4o Mini Transcribe makes fewer errors by 1.61 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and GPT-4o Mini Transcribe 4.45%, with likely ranges of 3.29 to 5.69% and 3.22 to 5.55%. 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-4o Mini Transcribe?
Yes. Deepgram Flux Multilingual finishes the transcript 516ms sooner. Deepgram Flux Multilingual has the final transcript 114ms after the speaker stops and GPT-4o Mini Transcribe 630ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux Multilingual or GPT-4o Mini Transcribe better for voice agents?
Deepgram Flux Multilingual finishes the transcript 516ms 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-4o Mini Transcribe stream text while the speaker is talking?
Only Deepgram Flux Multilingual. GPT-4o Mini Transcribe returns no text until the speaker stops, which matters if your agent reacts to partial transcripts.