AssemblyAI Universal 3.6 Pro vs GPT-4o Mini Transcribe
AssemblyAI Universal 3.6 Pro is ahead of GPT-4o Mini Transcribe on the Pipecat Dataset 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
AssemblyAI Universal 3.6 Pro is the better pick over GPT-4o Mini Transcribe on every measure that separates them: accuracy on clean, scripted audio (1.58 points lower) and quick turn-taking once the caller stops (539ms 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
- AssemblyAI Universal 3.6 Pro
- 1.77%1st
- GPT-4o Mini Transcribe
- 3.35%11th
AssemblyAI Universal 3.6 Pro, 1.58 points lower
- AssemblyAI Universal 3.6 Pro
- 4.50%14th
- GPT-4o Mini Transcribe
- 4.45%12th
Too close to call: inside the margin of error
- AssemblyAI Universal 3.6 Pro
- 485ms1st
- GPT-4o Mini Transcribe
- No text until the speaker stops
Not comparable
- AssemblyAI Universal 3.6 Pro
- 91ms3rd
- GPT-4o Mini Transcribe
- 630ms16th
AssemblyAI Universal 3.6 Pro, 539ms 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
AssemblyAI Universal 3.6 Pro shows first text after 967ms and final text after 232ms, 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 | AssemblyAI Universal 3.6 Pro | 485ms | 966ms | 967ms |
| GPT-4o Mini Transcribe | No text until the speaker stops | |||
| Final-text delay | AssemblyAI Universal 3.6 Pro | 91ms | 210ms | 232ms |
| 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 | Universal 3.6 Pro | 4o Mini Transcribe |
|---|---|---|
| Pipecat Dataset WER | 1.77%1st of 17 | 3.35%11th of 17 |
| Ocular Dataset WER | 4.50%14th of 17 | 4.45%12th of 17 |
| Time to first text | 485ms1st of 15 | Not comparable |
| Final-text delay | 91ms3rd of 17 | 630ms16th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| AssemblyAI Universal 3.6 Pro | 1,000 | 8 | 206 | 0 |
| 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: AssemblyAI vs OpenAI
- AssemblyAI Universal 3.6 Pro vs AssemblyAI Universal 3.5 Pro
- AssemblyAI Universal 3.6 Pro vs Google Chirp 3
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- AssemblyAI Universal 3.6 Pro vs GPT Realtime Whisper
- AssemblyAI Universal 3.6 Pro vs Cartesia Ink 2
- AssemblyAI Universal 3.6 Pro vs Speechmatics Linden
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- AssemblyAI Universal 3.6 Pro vs Inworld STT-1
- AssemblyAI Universal 3.6 Pro vs Deepgram Nova-3
- AssemblyAI Universal 3.6 Pro vs Smallest Pulse
- AssemblyAI Universal 3.6 Pro vs GPT-4o Transcribe
- AssemblyAI Universal 3.6 Pro vs Deepgram Flux English
- AssemblyAI Universal 3.6 Pro vs Deepgram Flux Multilingual
- AssemblyAI Universal 3.6 Pro 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 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 Deepgram Flux Multilingual
- GPT-4o Mini Transcribe vs Gradium
Frequently asked questions
Which is more accurate, AssemblyAI Universal 3.6 Pro or GPT-4o Mini Transcribe?
AssemblyAI Universal 3.6 Pro is more accurate on the public clips. On the 1,000 public Pipecat clips, AssemblyAI Universal 3.6 Pro scores 1.77% and GPT-4o Mini Transcribe 3.35%, so AssemblyAI Universal 3.6 Pro makes fewer errors by 1.58 points. On the eight licensed Ocular recordings, AssemblyAI Universal 3.6 Pro scores 4.50% and GPT-4o Mini Transcribe 4.45%, with likely ranges of 3.66 to 5.34% 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 AssemblyAI Universal 3.6 Pro faster than GPT-4o Mini Transcribe?
Yes. AssemblyAI Universal 3.6 Pro finishes the transcript 539ms sooner. AssemblyAI Universal 3.6 Pro has the final transcript 91ms after the speaker stops and GPT-4o Mini Transcribe 630ms, at the median. Both are measured on the same 206 streamed turns.
Is AssemblyAI Universal 3.6 Pro or GPT-4o Mini Transcribe better for voice agents?
AssemblyAI Universal 3.6 Pro finishes the transcript 539ms 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 AssemblyAI Universal 3.6 Pro and GPT-4o Mini Transcribe stream text while the speaker is talking?
Only AssemblyAI Universal 3.6 Pro. GPT-4o Mini Transcribe returns no text until the speaker stops, which matters if your agent reacts to partial transcripts.