Google Chirp 2 vs GPT Realtime Whisper
GPT Realtime Whisper is ahead of Google Chirp 2 on all four metrics.
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.
- Google Chirp 2
- 2.59%7th
- GPT Realtime Whisper
- 2.16%4th
GPT Realtime Whisper, 0.44 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Google Chirp 2
- 3.80%5th
- GPT Realtime Whisper
- 3.53%4th
GPT Realtime Whisper, 0.27 points lower
Median delay before the first words arrive, from the first audio packet.
- Google Chirp 2
- 4.50s13th
- GPT Realtime Whisper
- 1.38s8th
GPT Realtime Whisper, 3.12s sooner
Median delay after the speaker stops before the transcript is final.
- Google Chirp 2
- 568ms13th
- GPT Realtime Whisper
- 543ms12th
GPT Realtime Whisper, 25ms 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 | Google Chirp 2 | 4.50s | 6.40s | 8.15s |
| GPT Realtime Whisper | 1.38s | 1.84s | 2.86s | |
| Final-text delay | Google Chirp 2 | 568ms* | 1.04s* | 1.22s* |
| GPT Realtime Whisper | 543ms | 647ms | 669ms |
* Final-text delay is the observed time after stream close, because controlled finalization is not available for this model. Compare it with care against models that finalize on request.
Both against every other model
Open on the metric where the two sit furthest apart, with the other three a tab away.
Time to first text, measured from the first audio packet. Lower is better.
| # | Model | First text | turns | |
|---|---|---|---|---|
| 1 | AssemblyAI Universal 3.5 Pro | 489ms | 205 | |
| 2 | Deepgram Flux English | 791ms | 199 | |
| 3 | Deepgram Flux Multilingual | 793ms | 175 | |
| 4 | Smallest Pulse | 1057ms | 206 | |
| 5 | Inworld STT-1 | 1063ms | 206 | |
| 6 | Deepgram Nova-3 | 1064ms | 203 | |
| 7 | Speechmatics Linden | 1190ms | 194 | |
| 8 | GPT Realtime Whisper | 1379ms | 206 | |
| 9 | Cartesia Ink 2 | 1511ms | 198 | |
| 10 | Gradium | 1705ms | 198 | |
| 11 | Reson8 | 2400ms | 201 | |
| 12 | Google Chirp 3 | 4480ms | 206 | |
| 13 | Google Chirp 2 | 4497ms | 205 |
Not ranked: GPT-4o Mini Transcribe and GPT-4o Transcribe return no text until the speaker stops, so first-text time is not comparable.
Median across the same 206 Ocular Dataset turns. Providers finalize differently, so compare models with similar finalization contracts before treating small gaps as meaningful.
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 |
|---|---|---|---|---|
| Google Chirp 2 | 1,000 | 8 | 205 | 1 |
| GPT Realtime Whisper | 1,000 | 8 | 206 | 0 |
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 Google Chirp 2 or GPT Realtime Whisper more accurate?
On the 1,000 public Pipecat clips, Google Chirp 2 scores 2.59% and GPT Realtime Whisper 2.16%, so GPT Realtime Whisper makes fewer errors by 0.44 points. On the eight licensed Ocular recordings, Google Chirp 2 scores 3.80% and GPT Realtime Whisper 3.53%, so GPT Realtime Whisper makes fewer errors by 0.27 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Google Chirp 2 or GPT Realtime Whisper?
GPT Realtime Whisper returns first text 3.12s sooner, at 1.38s against 4.50s at the median. GPT Realtime Whisper has the final transcript 25ms sooner, 543ms after the speaker stops against 568ms. Both are measured on the same 206 streamed turns.
How were Google Chirp 2 and GPT Realtime Whisper 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. Google Chirp 2 returned usable text for 1,000 of the public clips and GPT Realtime Whisper for 1,000. Latency uses each provider's own supported finalization contract.