Deepgram Flux Multilingual vs Google Chirp 2
Deepgram Flux Multilingual 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 Deepgram Flux Multilingual vs Google Chirp 3.
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 Multilingual for showing words while the caller is still talking (3.70s sooner) and quick turn-taking once the caller stops (454ms sooner). Pick Google Chirp 2 for accuracy on clean, scripted audio (2.37 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 Multilingual
- 4.96%16th
- Google Chirp 2
- 2.59%9th
Google Chirp 2, 2.37 points lower
- Deepgram Flux Multilingual
- 4.53%15th
- Google Chirp 2
- 3.80%5th
Too close to call: inside the margin of error
- Deepgram Flux Multilingual
- 793ms4th
- Google Chirp 2
- 4.50s15th
Deepgram Flux Multilingual, 3.70s sooner
- Deepgram Flux Multilingual
- 114ms6th
- Google Chirp 2
- 568ms15th
Deepgram Flux Multilingual, 454ms 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 Multilingual shows first text after 3.41s and final text after 141ms, and Google Chirp 2 shows first text after 8.15s and final text after 1.22s, on a bad turn (P95) across the same streamed turns.
| Measure | Model | P50 | P90 | P95 |
|---|---|---|---|---|
| Time to first text | Deepgram Flux Multilingual | 793ms | 2.00s | 3.41s |
| Google Chirp 2 | 4.50s | 6.40s | 8.15s | |
| Final-text delay | Deepgram Flux Multilingual | 114ms | 135ms | 141ms |
| Google Chirp 2 | 568ms* | 1.04s* | 1.22s* |
- 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 Multilingual | Chirp 2 |
|---|---|---|
| Pipecat Dataset WER | 4.96%16th of 17 | 2.59%9th of 17 |
| Ocular Dataset WER | 4.53%15th of 17 | 3.80%5th of 17 |
| Time to first text | 793ms4th of 15 | 4.50s15th of 15 |
| Final-text delay | 114ms6th of 17 | 568ms15th of 17 |
How much audio each model was tested on
| Model | Pipecat clips | Ocular recordings | Turns timed | Failed |
|---|---|---|---|---|
| Deepgram Flux Multilingual | 999 | 8 | 175 | 31 |
| Google Chirp 2 | 1,000 | 8 | 205 | 1 |
Only clips with usable text count, so the two totals can differ. How WER is scored.
Related comparisons
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- Deepgram Flux Multilingual vs Inworld STT-1
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- Deepgram Flux Multilingual vs Speechmatics Linden
- Deepgram Flux Multilingual vs Cartesia Ink 2
- Deepgram Flux Multilingual vs GPT Realtime Whisper
- Deepgram Flux Multilingual vs Reson8
- Deepgram Flux Multilingual vs Google Chirp 3
- Deepgram Flux Multilingual vs AssemblyAI Universal 3.6 Pro
- Google Chirp 2 vs Google Chirp 3
Frequently asked questions
Which is more accurate, Deepgram Flux Multilingual or Google Chirp 2?
Google Chirp 2 is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Flux Multilingual scores 4.96% and Google Chirp 2 2.59%, so Google Chirp 2 makes fewer errors by 2.37 points. On the eight licensed Ocular recordings, Deepgram Flux Multilingual scores 4.53% and Google Chirp 2 3.80%, with likely ranges of 3.29 to 5.69% and 2.93 to 4.60%. 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 Multilingual faster than Google Chirp 2?
Yes. Deepgram Flux Multilingual finishes the transcript 454ms sooner. Deepgram Flux Multilingual has the final transcript 114ms after the speaker stops and Google Chirp 2 568ms, at the median. Both are measured on the same 206 streamed turns.
Is Deepgram Flux Multilingual or Google Chirp 2 better for voice agents?
Deepgram Flux Multilingual finishes the transcript 454ms 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 Multilingual and Google Chirp 2 stream text while the speaker is talking?
Yes, both return partial text before the speaker stops. Deepgram Flux Multilingual shows first text after 793ms and Google Chirp 2 after 4.50s, at the median, so Deepgram Flux Multilingual is 3.70s sooner.