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OpenAI's speech-to-text model with built-in speaker diarization, purpose-built for labelling who says what in multi-speaker audio.
Overview. GPT-4o Transcribe Diarize is OpenAI's automatic speech recognition model with built-in speaker diarization, served exclusively through the Transcription API rather than as a general chat endpoint. It takes an audio file and returns a text transcript, optionally labelling which speaker said what. Reach for it when a workload specifically needs speaker-attributed transcripts rather than plain speech-to-text.
Key strengths. The model's core strength is combining transcription with diarization in a single pass, so segments of audio are already tagged by speaker in the output. It supports a diarized_json response format for structured speaker-labeled results, and it accepts optional short reference clips to map audio segments onto known, named speakers rather than generic speaker IDs. OpenAI has positioned it as the model going forward for real-time transcription in the Transcription API as several older transcription models are retired.
When to use it. Use this model when you need to know who said what in a recording — multi-speaker calls, meetings, interviews, or real-time conversational audio streams. It is not the right choice for single-speaker or simple file transcription where diarization adds no value, and it isn't a substitute for a general chat or reasoning model since it doesn't accept prompts or perform any task beyond transcription and speaker labelling.
Limitations. The model has a much smaller context window (16,000 tokens) and output ceiling (2,000 tokens) than the general-purpose GPT-4o line, reflecting its narrow ASR scope. It doesn't accept system prompts, doesn't perform tool calling, and produces text-only output — there's no audio synthesis, vision input, or agentic capability here.
Sovereignty note. OpenAI is a US-based creator, and as with the rest of the GPT-4o family, this model carries CLOUD Act exposure — a relevant factor for buyers with strict data residency requirements. It is a closed-weight, proprietary model with no Sovereign self-hosting option; Inference runs entirely on OpenAI's own serving stack.
| Inference Provider | Sovereignty | Runs in | Price | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.00539 per audio minute | Online |
5 use cases classified by the curator. The note says why.
Model name and specialization signal a transcribe-and-diarize variant purpose-built for speech-to-text with speaker separation — the core use case for this model.
Diarization output (speaker-labeled transcripts) is a strong upstream input for meeting-summary pipelines, though this variant itself doesn't summarize — it feeds that step rather than performing it.
This is a transcription/diarization model (speech-to-text), not a text-to-speech generator — no synthesis capability implied by its name or role.
Lacks the conversational/generation capability a voice agent requires — this is an input-side transcription specialist, not an interactive dialogue model.
No generative-voice capability is indicated; this variant is built for transcribing and diarizing existing audio, not producing synthetic voice.
Sourced numbers, not our own evaluations. Percentiles are against the transcription models on Stav.
No benchmark source yet
curl https://api.stav.ai/v1/audio/transcriptions \
-H "Authorization: Bearer $STAV_API_KEY" \
-F model="openai/gpt-4o-transcribe-diarize" \
-F file="@meeting.mp3"→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at