Left gets fewer words wrong. Lower has the final text ready sooner.
Cartesia Ink 2 is ahead on both datasets together and the Pipecat Dataset, and Deepgram Flux Multilingual on time to first text and final-text delay. 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 its voices, see our notes on Cartesia text to speech.
Cartesia Ink 2 is more accurate on both datasets. On the 1,000 public Pipecat clips, Cartesia Ink 2 scores 2.63% and Deepgram Flux Multilingual 4.81%, so Cartesia Ink 2 makes fewer errors by 2.18 points. On the eight licensed Ocular recordings, Cartesia Ink 2 scores 2.39% and Deepgram Flux Multilingual 4.96%, so Cartesia Ink 2 makes fewer errors by 2.57 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
Cartesia Ink 2 is more accurate on both datasets. On the 1,000 public Pipecat clips, Cartesia Ink 2 scores 2.63% and Deepgram Flux Multilingual 4.81%, so Cartesia Ink 2 makes fewer errors by 2.18 points. On the eight licensed Ocular recordings, Cartesia Ink 2 scores 2.39% and Deepgram Flux Multilingual 4.96%, so Cartesia Ink 2 makes fewer errors by 2.57 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
No. Deepgram Flux Multilingual finishes the transcript 718ms sooner. Cartesia Ink 2 has the final transcript 1.51s after the speaker stops and Deepgram Flux Multilingual 793ms, at the median. Both are measured on the same 206 streamed turns.
It is a trade-off. Cartesia Ink 2 makes fewer errors on real conversations, while Deepgram Flux Multilingual finishes the transcript 718ms 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. Cartesia Ink 2 shows first text after 3.09% and Deepgram Flux Multilingual after 4.53%, at the median.
Cekura runs these simulated calls on your agent and scores every one.