AssemblyAI Universal 3.5 Pro vs Deepgram Flux English
AssemblyAI Universal 3.5 Pro is ahead on the Pipecat Dataset and time to first text, and Deepgram Flux English on the Ocular Dataset and final-text delay.
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
The four metrics, side by side
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset.
- AssemblyAI Universal 3.5 Pro
- 1.93%1st
- Deepgram Flux English
- 3.90%13th
AssemblyAI Universal 3.5 Pro, 1.97 points lower
Word error rate on eight licensed recordings provided by Ocular.
- AssemblyAI Universal 3.5 Pro
- 4.23%9th
- Deepgram Flux English
- 4.18%7th
Deepgram Flux English, 0.05 points lower
Median delay before the first words arrive, from the first audio packet.
- AssemblyAI Universal 3.5 Pro
- 489ms1st
- Deepgram Flux English
- 791ms2nd
AssemblyAI Universal 3.5 Pro, 302ms sooner
Median delay after the speaker stops before the transcript is final.
- AssemblyAI Universal 3.5 Pro
- 180ms7th
- Deepgram Flux English
- 108ms3rd
Deepgram Flux English, 72ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field.
First text and final text, at three percentiles
Both measured on the same 206 streamed Ocular Dataset turns. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | AssemblyAI Universal 3.5 Pro | 489ms | 966ms | 1.08s |
| Deepgram Flux English | 791ms | 1.23s | 1.51s | |
| Final-text delay | AssemblyAI Universal 3.5 Pro | 180ms | 532ms | 692ms |
| Deepgram Flux English | 108ms | 132ms | 141ms |
Both against every other model
Open on the metric where the two sit furthest apart, with the other three a tab away.
Word error rate on 1,000 clips from Pipecat's STT benchmark dataset. Lower is better.
| # | Model | Pipecat Dataset | clips | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 1.93% | 1,000 | |
| 2 | Google Chirp 3 | 2.00% | 1,000 | |
| 3 | Reson8 | 2.10% | 1,000 | |
| 4 | GPT Realtime Whisper | 2.16% | 1,000 | |
| 5 | Cartesia Ink 2 | 2.39% | 1,000 | |
| 6 | Speechmatics Linden | 2.46% | 998 | |
| 7 | Google Chirp 2 | 2.59% | 1,000 | |
| 8 | Inworld STT-1 | 2.72% | 1,000 | |
| 9 | GPT-4o Mini Transcribe | 3.35% | 1,000 | |
| 10 | Deepgram Nova-3 | 3.46% | 1,000 | |
| 11 | Smallest Pulse | 3.66% | 1,000 | |
| 12 | GPT-4o Transcribe | 3.90% | 1,000 | |
| 13 | Deepgram Flux English | 3.90% | 999 | |
| 14 | Deepgram Flux Multilingual | 4.96% | 999 | |
| 15 | Gradium | 6.48% | 1,000 |
Counts are the clips each model returned usable text for, so they differ between models and between datasets.
Both on the other two views
Pipecat vs Ocular accuracy
Every model's error rate on the public clips, against the same model on real conversations.
Transcription response time
How soon each model returns its first words, against how soon its transcript is final.
What the numbers rest on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| AssemblyAI Universal 3.5 Pro | 1,000 | 8 | 205 | 1 |
| Deepgram Flux English | 999 | 8 | 199 | 7 |
A clip counts only where the model returned usable text, so counts differ between models. Word error rate is measured the same way throughout: incorrect, missing, and extra words against the reference transcript.
Frequently asked questions
Is AssemblyAI Universal 3.5 Pro or Deepgram Flux English more accurate?
On the 1,000 public Pipecat clips, AssemblyAI Universal 3.5 Pro scores 1.93% and Deepgram Flux English 3.90%, so AssemblyAI Universal 3.5 Pro makes fewer errors by 1.97 points. On the eight licensed Ocular recordings, AssemblyAI Universal 3.5 Pro scores 4.23% and Deepgram Flux English 4.18%, so Deepgram Flux English makes fewer errors by 0.05 points. The two datasets disagree, so the better choice depends on whether your audio looks more like clean public clips or real conversations.
Which is faster, AssemblyAI Universal 3.5 Pro or Deepgram Flux English?
AssemblyAI Universal 3.5 Pro returns first text 302ms sooner, at 489ms against 791ms at the median. Deepgram Flux English has the final transcript 72ms sooner, 108ms after the speaker stops against 180ms. Both are measured on the same 206 streamed turns.
How were AssemblyAI Universal 3.5 Pro and Deepgram Flux English compared?
Both ran the same two datasets under the same conditions as every other model in the benchmark: 1,000 public clips from Pipecat's STT benchmark dataset and eight licensed conversation recordings from Ocular. AssemblyAI Universal 3.5 Pro returned usable text for 1,000 of the public clips and Deepgram Flux English for 999. Latency uses each provider's own supported finalization contract.