Deepgram Nova-3 vs Gradium

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Accuracy and speed

Left gets fewer words wrong. Lower has the final text ready sooner.

Accurate and fast0ms200ms400ms600ms800ms0%2%4%6%8%Words wrong on clean audio (Pipecat WER)
  • Nova-33.46% · 103ms
  • Gradium6.48% · 212ms

Deepgram Nova-3 is ahead of Gradium on both datasets together, the Pipecat Dataset, time to first text and final-text delay. The Ocular Dataset is too close to call.

Last updated Methodology by Dileep Chagam

Who is better at what

Nova-3Gradium
  • Overall WER3.78%5.78%
  • Pipecat Dataset WER3.46%6.48%
  • Ocular Dataset WER4.39%4.45%
  • Time to first text1.06s1.71s
  • Final-text delay103ms212ms

Lower is better on every measure. Too close to call means the likely ranges overlap. For costs, see our breakdown of Deepgram pricing.

Nova-3 vs Gradium FAQ

  • Deepgram Nova-3 is more accurate on both datasets. On the 1,000 public Pipecat clips, Deepgram Nova-3 scores 3.78% and Gradium 5.78%, so Deepgram Nova-3 makes fewer errors by 1.99 points. On the eight licensed Ocular recordings, Deepgram Nova-3 scores 3.46% and Gradium 6.48%, so Deepgram Nova-3 makes fewer errors by 3.01 points. Word error rate counts incorrect, missing, and extra words, so lower is better.

  • Yes. Deepgram Nova-3 finishes the transcript 641ms sooner. Deepgram Nova-3 has the final transcript 1.06s after the speaker stops and Gradium 1.71s, at the median. Both are measured on the same 206 streamed turns.

  • Deepgram Nova-3 is the stronger pick for voice agents: it makes fewer errors on real conversations and finishes the transcript 641ms 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 Gradium after 4.45%, at the median.

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