Google Chirp 3 vs Inworld STT-1
Google Chirp 3 is ahead on the Pipecat Dataset and the Ocular Dataset, and Inworld STT-1 on time to first text 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.
- Google Chirp 3
- 2.00%2nd
- Inworld STT-1
- 2.72%8th
Google Chirp 3, 0.71 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Google Chirp 3
- 4.19%8th
- Inworld STT-1
- 10.60%14th
Google Chirp 3, 6.42 points lower
Median delay before the first words arrive, from the first audio packet.
- Google Chirp 3
- 4.48s12th
- Inworld STT-1
- 1.06s5th
Inworld STT-1, 3.42s sooner
Median delay after the speaker stops before the transcript is final.
- Google Chirp 3
- 475ms11th
- Inworld STT-1
- 41ms1st
Inworld STT-1, 434ms 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 3 | 4.48s | 6.21s | 7.97s |
| Inworld STT-1 | 1.06s | 1.07s | 1.07s | |
| Final-text delay | Google Chirp 3 | 475ms* | 941ms* | 1.41s* |
| Inworld STT-1 | 41ms | 60ms | 65ms |
* 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.
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.
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 3 | 1,000 | 8 | 206 | 0 |
| Inworld STT-1 | 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 3 or Inworld STT-1 more accurate?
On the 1,000 public Pipecat clips, Google Chirp 3 scores 2.00% and Inworld STT-1 2.72%, so Google Chirp 3 makes fewer errors by 0.71 points. On the eight licensed Ocular recordings, Google Chirp 3 scores 4.19% and Inworld STT-1 10.60%, so Google Chirp 3 makes fewer errors by 6.42 points. Word error rate counts incorrect, missing, and extra words against the reference transcript, so lower is better.
Which is faster, Google Chirp 3 or Inworld STT-1?
Inworld STT-1 returns first text 3.42s sooner, at 1.06s against 4.48s at the median. Inworld STT-1 has the final transcript 434ms sooner, 41ms after the speaker stops against 475ms. Both are measured on the same 206 streamed turns.
How were Google Chirp 3 and Inworld STT-1 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 3 returned usable text for 1,000 of the public clips and Inworld STT-1 for 1,000. Latency uses each provider's own supported finalization contract.