Deepgram Flux English vs Google Chirp 3
Deepgram Flux English is ahead on time to first text and final-text delay, and Google Chirp 3 on the Pipecat Dataset. The Ocular Dataset is too close to call.
Last updated Methodology by Dileep Chagam
Accuracy vs speed
Each dot is one model. Bottom-left is best: fewer errors and a faster final transcript.
Which to pick
Pick Deepgram Flux English for showing words while the caller is still talking (3.69s sooner) and quick turn-taking once the caller stops (367ms sooner). Pick Google Chirp 3 for accuracy on clean, scripted audio (1.90 points lower). For accuracy on real phone conversations, the gap is inside the margin of error, so neither is ahead.
The four metrics, side by side
- Deepgram Flux English
- 3.90%15th
- Google Chirp 3
- 2.00%3rd
Google Chirp 3, 1.90 points lower
- Deepgram Flux English
- 4.18%8th
- Google Chirp 3
- 4.19%9th
Too close to call: inside the margin of error
- Deepgram Flux English
- 791ms3rd
- Google Chirp 3
- 4.48s14th
Deepgram Flux English, 3.69s sooner
- Deepgram Flux English
- 108ms5th
- Google Chirp 3
- 475ms13th
Deepgram Flux English, 367ms sooner
Lower is better on every metric. The small figure is each model's place in the whole field. What each metric measures.
- WER · Word error rate
- Share of words the model got wrong: replaced, missing or added. Lower is better.
- Pipecat Dataset
- 1,000 short public clips from Pipecat's STT benchmark dataset.
- Ocular Dataset
- 8 recordings from 4 real phone conversations, licensed from Ocular.
- TTFT · Time to first text
- How long after the audio starts streaming until the first words appear.
- TTFS · Final-text delay
- How long after the caller stops until the full transcript arrives. A voice agent waits for this before it replies.
First text and final text, at three percentiles
Deepgram Flux English shows first text after 1.51s and final text after 141ms, and Google Chirp 3 shows first text after 7.97s and final text after 1.41s, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Deepgram Flux English | 791ms | 1.23s | 1.51s |
| Google Chirp 3 | 4.48s | 6.21s | 7.97s | |
| Final-text delay | Deepgram Flux English | 108ms | 132ms | 141ms |
| Google Chirp 3 | 475ms* | 941ms* | 1.41s* |
- P50 / P90 / P95 · Typical, slow and slowest turns
- Half of turns are faster than P50. Only 1 in 10 is slower than P90, and 1 in 20 slower than P95.
* 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.
How each ranks among all 17 models
Place among the 17 models on each metric. Lower is better on all four.
| Metric | Flux English | Chirp 3 |
|---|---|---|
| Pipecat Dataset WER | 3.90%15th of 17 | 2.00%3rd of 17 |
| Ocular Dataset WER | 4.18%8th of 17 | 4.19%9th of 17 |
| Time to first text | 791ms3rd of 15 | 4.48s14th of 15 |
| Final-text delay | 108ms5th of 17 | 475ms13th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux English | 999 | 8 | 199 | 7 |
| Google Chirp 3 | 1,000 | 8 | 206 | 0 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
Both providers, every model: Deepgram vs Google
- Deepgram Flux English vs GPT-4o Transcribe
- Deepgram Flux English vs Smallest Pulse
- Deepgram Flux English vs Deepgram Nova-3
- Deepgram Flux English vs GPT-4o Mini Transcribe
- Deepgram Flux English vs Deepgram Flux Multilingual
- Deepgram Flux English vs Inworld STT-1
- Deepgram Flux English vs Soniox STT-RT v5
- Deepgram Flux English vs Speechmatics Linden
- Deepgram Flux English vs Cartesia Ink 2
- Deepgram Flux English vs GPT Realtime Whisper
- Deepgram Flux English vs Reson8
- Deepgram Flux English vs AssemblyAI Universal 3.6 Pro
- Deepgram Flux English vs Gradium
- Google Chirp 3 vs Reson8
- Google Chirp 3 vs GPT Realtime Whisper
- Google Chirp 3 vs AssemblyAI Universal 3.6 Pro
- Google Chirp 3 vs Cartesia Ink 2
- Google Chirp 3 vs Speechmatics Linden
- Google Chirp 3 vs Soniox STT-RT v5
- Google Chirp 3 vs Google Chirp 2
- Google Chirp 3 vs Inworld STT-1
- Google Chirp 3 vs GPT-4o Mini Transcribe
- Google Chirp 3 vs Deepgram Nova-3
- Google Chirp 3 vs Smallest Pulse
- Google Chirp 3 vs GPT-4o Transcribe
- Google Chirp 3 vs Deepgram Flux Multilingual
- Google Chirp 3 vs Gradium
Frequently asked questions
Which is more accurate, Deepgram Flux English or Google Chirp 3?
Google Chirp 3 is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux English scores 3.90% and Google Chirp 3 2.00%, so Google Chirp 3 makes fewer errors by 1.90 points. On the eight licensed Ocular recordings, Deepgram Flux English scores 4.18% and Google Chirp 3 4.19%, with likely ranges of 3.08 to 5.23% and 2.97 to 5.27%. The ranges overlap, so the difference is too close to call. Word error rate counts incorrect, missing, and extra words, so lower is better.
Is Deepgram Flux English faster than Google Chirp 3?
Yes. Deepgram Flux English finishes the transcript 367ms sooner. Deepgram Flux English has the final transcript 108ms after the speaker stops and Google Chirp 3 475ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux English or Google Chirp 3 better for voice agents?
Deepgram Flux English finishes the transcript 367ms 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.
Do Deepgram Flux English and Google Chirp 3 stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux English shows first text after 791ms and Google Chirp 3 after 4.48s, at the median, so Deepgram Flux English is 3.69s sooner.