Google Chirp 2 vs Gradium
Google Chirp 2 is ahead on the Pipecat Dataset and the Ocular Dataset, and Gradium 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 2
- 2.59%7th
- Gradium
- 6.48%15th
Google Chirp 2, 3.89 points lower
Word error rate on eight licensed recordings provided by Ocular.
- Google Chirp 2
- 3.80%5th
- Gradium
- 4.45%11th
Google Chirp 2, 0.65 points lower
Median delay before the first words arrive, from the first audio packet.
- Google Chirp 2
- 4.50s13th
- Gradium
- 1.71s10th
Gradium, 2.79s sooner
Median delay after the speaker stops before the transcript is final.
- Google Chirp 2
- 568ms13th
- Gradium
- 212ms9th
Gradium, 356ms 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 |
| 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.
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 |
|---|---|---|---|---|
| Google Chirp 2 | 1,000 | 8 | 205 | 1 |
| Gradium | 1,000 | 8 | 198 | 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 Gradium more accurate?
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%, so Google Chirp 2 makes fewer errors by 0.65 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 Gradium?
Gradium returns first text 2.79s sooner, at 1.71s against 4.50s at the median. Gradium has the final transcript 356ms sooner, 212ms after the speaker stops against 568ms. Both are measured on the same 206 streamed turns.
How were Google Chirp 2 and Gradium 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 Gradium for 1,000. Latency uses each provider's own supported finalization contract.