Left gets fewer words wrong. Lower has the final text ready sooner.
GPT Realtime Whisper is ahead of GPT-4o Mini Transcribe on both datasets together, the Pipecat Dataset 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. Our guide covers testing OpenAI Realtime API voice agents.
GPT Realtime Whisper is more accurate on both datasets. On the 1,000 public Pipecat clips, GPT-4o Mini Transcribe scores 3.73% and GPT Realtime Whisper 2.63%, so GPT Realtime Whisper makes fewer errors by 1.10 points. On the eight licensed Ocular recordings, GPT-4o Mini Transcribe scores 3.35% and GPT Realtime Whisper 2.16%, so GPT Realtime Whisper makes fewer errors by 1.20 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
GPT Realtime Whisper is more accurate on both datasets. On the 1,000 public Pipecat clips, GPT-4o Mini Transcribe scores 3.73% and GPT Realtime Whisper 2.63%, so GPT Realtime Whisper makes fewer errors by 1.10 points. On the eight licensed Ocular recordings, GPT-4o Mini Transcribe scores 3.35% and GPT Realtime Whisper 2.16%, so GPT Realtime Whisper makes fewer errors by 1.20 points. 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.
GPT Realtime Whisper makes fewer errors on real conversations, and the two are not separated on speed. A voice agent cannot reply until the final transcript arrives, and the Ocular recordings are real conversations.
Only GPT Realtime Whisper. 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.