Gradium is ahead on time to first text and final-text delay, and Google Chirp 2 on the Pipecat Dataset. The Ocular Dataset is too close to call.
Google Chirp 2 has a newer release. See Google Chirp 3 vs Gradium.
Last updated Methodology by Dileep Chagam
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
Pick Google Chirp 2 for accuracy on clean, scripted audio (3.89 points lower). Pick Gradium for showing words while the caller is still talking (2.79s sooner) and quick turn-taking once the caller stops (356ms sooner). For accuracy on real phone conversations, the gap is inside the margin of error, so neither is ahead.
Google Chirp 2, 3.89 points lower
Too close to call: inside the margin of error
Gradium, 2.79s sooner
Gradium, 356ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field. What each metric measures.
Google Chirp 2 shows first text after 8.15s and final text after 1.22s, and Gradium shows first text after 7.32s and final text after 280ms, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Google Chirp 2 | 4.50s | 6.40s | 8.15s |
| Gradium | 1.71s | 4.05s | 7.32s | |
| Final-text delay | Google Chirp 2 | 568ms* | 1.04s* | 1.22s* |
| Gradium | 212ms | 266ms | 280ms |
* 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.
Place among the 17 models on each metric. Lower is better on all four.
| Metric | Chirp 2 | Gradium |
|---|---|---|
| Pipecat Dataset WER | 2.59%9th of 17 | 6.48%17th of 17 |
| Ocular Dataset WER | 3.80%5th of 17 | 4.45%12th of 17 |
| Time to first text | 4.50s15th of 15 | 1.71s12th of 15 |
| Final-text delay | 568ms15th of 17 | 212ms11th of 17 |
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Google Chirp 2 | 1,000 | 8 | 205 | 1 |
| Gradium | 1,000 | 8 | 198 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Google Chirp 2 is more accurate on the public clips. On the 1,000 public Pipecat clips, Google Chirp 2 scores 2.59% and Gradium 6.48%, so Google Chirp 2 makes fewer errors by 3.89 points. On the eight licensed Ocular recordings, Google Chirp 2 scores 3.80% and Gradium 4.45%, with likely ranges of 2.93 to 4.60% and 3.17 to 5.90%. The ranges overlap, so the difference is too close to call. Word error rate counts incorrect, missing, and extra words, so lower is better.
No. Gradium finishes the transcript 356ms sooner. Google Chirp 2 has the final transcript 568ms after the speaker stops and Gradium 212ms, at the median. Both are measured on the same 206 streamed turns.
Gradium finishes the transcript 356ms 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.
Yes, both return partial text before the speaker stops. Google Chirp 2 shows first text after 4.50s and Gradium after 1.71s, at the median, so Gradium is 2.79s sooner.