Left gets fewer words wrong. Lower has the final text ready sooner.
Inworld STT-1 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
Lower is better on every measure. Too close to call means the likely ranges overlap. Our guide explains how word error rate is measured.
GPT-4o Mini Transcribe is more accurate on the public clips. On the 1,000 public Pipecat clips, Inworld STT-1 scores 5.44% and GPT-4o Mini Transcribe 3.73%, so GPT-4o Mini Transcribe makes fewer errors by 1.71 points. On the eight licensed Ocular recordings, Inworld STT-1 scores 2.72% and GPT-4o Mini Transcribe 3.35%, with likely ranges of 1.67 to 4.68% 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.
GPT-4o Mini Transcribe is more accurate on the public clips. On the 1,000 public Pipecat clips, Inworld STT-1 scores 5.44% and GPT-4o Mini Transcribe 3.73%, so GPT-4o Mini Transcribe makes fewer errors by 1.71 points. On the eight licensed Ocular recordings, Inworld STT-1 scores 2.72% and GPT-4o Mini Transcribe 3.35%, with likely ranges of 1.67 to 4.68% 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.
Inworld STT-1 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 Inworld STT-1. GPT-4o Mini Transcribe returns no text until the speaker stops, which matters if your agent reacts to partial transcripts.
Cekura runs these simulated calls on your agent and scores every one.