Deepgram Nova-3
Deepgram Nova-3 is 2nd of 15 on final-text delay and 10th of 15 on the Ocular Dataset.
- 3.46%
- Pipecat WER · 10th
- 4.39%
- Ocular WER · 10th
- 1.06s
- First text · 6th
- 103ms
- Final text · 2nd
Quality and speed frontier
Every model's error rate on the public clips, against how long its transcript takes to settle.
Deepgram Nova-3 against every other model
Open on the metric Deepgram Nova-3 places highest on, with the other three a tab away. Lower is better on all of them, so the order runs best first.
Final-text delay after the speaker stops. Lower is better.
| # | Model | Final text | turns | |
|---|---|---|---|---|
| 1 | Inworld STT-1 | 41ms | 206 | |
| 2 | Deepgram Nova-3 | 103ms | 202 | |
| 3 | Deepgram Flux English | 108ms | 199 | |
| 4 | Deepgram Flux Multilingual | 114ms | 175 | |
| 5 | Cartesia Ink 2 | 123ms | 198 | |
| 6 | Speechmatics Linden | 147ms | 194 | |
| 7 | AssemblyAI Universal 3.5 Pro | 180ms | 205 | |
| 8 | Smallest Pulse | 201ms | 206 | |
| 9 | Gradium | 212ms | 198 | |
| 10 | Reson8 | 235ms | 201 | |
| 11 | Google Chirp 3 | 475ms | 206 | |
| 12 | GPT Realtime Whisper | 543ms | 206 | |
| 13 | Google Chirp 2 | 568ms | 205 | |
| 14 | GPT-4o Mini Transcribe | 630ms | 206 | |
| 15 | GPT-4o Transcribe | 652ms | 206 |
Median across the same 206 Ocular Dataset turns. Providers finalize differently, so compare models with similar finalization contracts before treating small gaps as meaningful.
First text and final text, at three percentiles
Measured on the same 206 streamed Ocular Dataset turns as every other model. P90 and P95 say what a caller meets on a bad turn, which a median hides.
| Measure | P50 | P90 | P95 |
|---|---|---|---|
| Time to first text | 1.06s | 1.10s | 2.07s |
| Final-text delay | 103ms | 143ms | 155ms |
Deepgram Nova-3 on the other two views
The same charts the benchmark page carries, with Deepgram Nova-3 ringed. Consistency across the two datasets is one reading; how fast a model starts against how fast it commits is the other.
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
- 1,000
- Pipecat clips scored
- 8
- Ocular recordings scored
- 202
- turns timed
- 0
- requests that failed
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
Where does Deepgram Nova-3 place in the benchmark?
Deepgram Nova-3 places highest on final-text delay, 2nd of 15 at 103ms. Its weakest placing is the Ocular Dataset, 10th of 15 at 4.39%.
How accurate is Deepgram Nova-3?
Deepgram Nova-3 transcribes the 1,000 public Pipecat clips at 3.46% word error rate and the eight licensed Ocular recordings at 4.39%. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
How fast is Deepgram Nova-3 in a live conversation?
First text arrives after 1.06s at the median, and the transcript is final 103ms after the speaker stops. Both are measured on the same 206 streamed turns as every other model, and the page shows P90 and P95 as well, which is what a caller meets on a bad turn.
How was Deepgram Nova-3 measured?
Every model runs the same two datasets under the same conditions: 1,000 public clips from Pipecat's STT benchmark dataset for accuracy at scale, and eight licensed conversation recordings from Ocular for real speech. Latency comes from 206 streamed turns, using each provider's own supported finalization contract. Deepgram Nova-3 returned usable text for 1,000 of the public clips.