Left gets fewer words wrong. Lower has the final text ready sooner.
Deepgram Nova-3 is ahead on time to first text and final-text delay, and Google Chirp 3 on both datasets together and the Pipecat Dataset. The Ocular Dataset is too close to call.
Last updated Methodology by Dileep Chagam
Lower is better on every measure. Too close to call means the likely ranges overlap. For costs, see our breakdown of Deepgram pricing.
Google Chirp 3 is more accurate on both datasets. On the 1,000 public Pipecat clips, Deepgram Nova-3 scores 3.78% and Google Chirp 3 2.76%, so Google Chirp 3 makes fewer errors by 1.03 points. On the eight licensed Ocular recordings, Deepgram Nova-3 scores 3.46% and Google Chirp 3 2.00%, so Google Chirp 3 makes fewer errors by 1.46 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
Google Chirp 3 is more accurate on both datasets. On the 1,000 public Pipecat clips, Deepgram Nova-3 scores 3.78% and Google Chirp 3 2.76%, so Google Chirp 3 makes fewer errors by 1.03 points. On the eight licensed Ocular recordings, Deepgram Nova-3 scores 3.46% and Google Chirp 3 2.00%, so Google Chirp 3 makes fewer errors by 1.46 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
Yes. Deepgram Nova-3 finishes the transcript 3.42s sooner. Deepgram Nova-3 has the final transcript 1.06s after the speaker stops and Google Chirp 3 4.48s, at the median. Both are measured on the same 206 streamed turns.
It is a trade-off. Google Chirp 3 makes fewer errors on real conversations, while Deepgram Nova-3 finishes the transcript 3.42s sooner. 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. Deepgram Nova-3 shows first text after 4.39% and Google Chirp 3 after 4.19%, at the median.
Cekura runs these simulated calls on your agent and scores every one.