Deepgram Nova-3 vs GPT-4o Mini Transcribe

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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
  • 4o Mini Transcribe3.35% · 630ms

Deepgram Nova-3 is ahead on final-text delay, and GPT-4o Mini Transcribe on both datasets together. The Pipecat Dataset and The Ocular Dataset are too close to call.

Last updated Methodology by Dileep Chagam

Who is better at what

Nova-34o Mini Transcribe
  • Overall WER3.78%3.73%
  • Pipecat Dataset WER3.46%3.35%
  • Ocular Dataset WER4.39%4.45%
  • Time to first text1.06sn/a
  • Final-text delay103ms630ms

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 4o Mini Transcribe FAQ

  • GPT-4o Mini Transcribe is more accurate on the public clips. On the 1,000 public Pipecat clips, Deepgram Nova-3 scores 3.78% and GPT-4o Mini Transcribe 3.73%, so GPT-4o Mini Transcribe makes fewer errors by 0.05 points. On the eight licensed Ocular recordings, Deepgram Nova-3 scores 3.46% and GPT-4o Mini Transcribe 3.35%, with likely ranges of 3.16 to 3.78% and 3.08 to 3.65%. The ranges overlap, so the difference is too close to call. Word error rate counts incorrect, missing, and extra words, so lower is better.

  • Final-text delay was not measured for one or both, so speed cannot be compared. Both are measured on the same 206 streamed turns.

  • Deepgram Nova-3 and GPT-4o Mini Transcribe are level on the two figures a voice agent depends on most. A voice agent cannot reply until the final transcript arrives, and the Ocular recordings are real conversations.

  • Only Deepgram Nova-3. GPT-4o Mini Transcribe returns no text until the speaker stops, which matters if your agent reacts to partial transcripts.

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