Deepgram Flux English vs GPT-4o Mini Transcribe
Deepgram Flux English is ahead of GPT-4o Mini Transcribe on final-text delay. The Pipecat Dataset and The Ocular Dataset are 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 GPT-4o Mini Transcribe on every measure that separates them: quick turn-taking once the caller stops (522ms sooner). For accuracy on clean, scripted audio and 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
- GPT-4o Mini Transcribe
- 3.35%11th
Too close to call: inside the margin of error
- Deepgram Flux English
- 4.18%8th
- GPT-4o Mini Transcribe
- 4.45%12th
Too close to call: inside the margin of error
- Deepgram Flux English
- 791ms3rd
- GPT-4o Mini Transcribe
- No text until the speaker stops
Not comparable
- Deepgram Flux English
- 108ms5th
- GPT-4o Mini Transcribe
- 630ms16th
Deepgram Flux English, 522ms 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 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 English | 791ms | 1.23s | 1.51s |
| GPT-4o Mini Transcribe | No text until the speaker stops | |||
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 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 English | 4o Mini Transcribe |
|---|---|---|
| Pipecat Dataset WER | 3.90%15th of 17 | 3.35%11th of 17 |
| Ocular Dataset WER | 4.18%8th of 17 | 4.45%12th of 17 |
| Time to first text | 791ms3rd of 15 | Not comparable |
| Final-text delay | 108ms5th of 17 | 630ms16th 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 |
| 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 English vs GPT-4o Transcribe
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- Deepgram Flux English vs Deepgram Flux Multilingual
- 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
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- 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
- 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 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 Deepgram Flux Multilingual
- GPT-4o Mini Transcribe vs Gradium
Frequently asked questions
Which is more accurate, Deepgram Flux English or GPT-4o Mini Transcribe?
Deepgram Flux English and GPT-4o Mini Transcribe are too close to call on accuracy. On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and GPT-4o Mini Transcribe 3.35%, with likely ranges of 3.53 to 4.29% and 3.08 to 3.65%. The ranges overlap, so the difference is too close to call. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and GPT-4o Mini Transcribe 4.45%, with likely ranges of 3.08 to 5.23% 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 English faster than GPT-4o Mini Transcribe?
Yes. Deepgram Flux English finishes the transcript 522ms sooner. Deepgram Flux English has the final transcript 108ms 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 English or GPT-4o Mini Transcribe better for voice agents?
Deepgram Flux English finishes the transcript 522ms 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 GPT-4o Mini Transcribe stream text while the speaker is talking?
Only Deepgram Flux English. GPT-4o Mini Transcribe returns no text until the speaker stops, which matters if your agent reacts to partial transcripts.