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AI model by OpenAI

GPT Transcribe for business

OpenAI's current speech-to-text model, replacing Whisper and the GPT-4o transcribe models, with streaming and keyword or language hints.

  • Reviewed on September 24, 2026
  • OpenAI

Capability tiersRelative, not benchmarks

Reasoning Very low
Coding Very low
Vision Very low
Speed High
Price level Low
Context size Low
Tool calling Very low
Structured output Very low

Key facts about GPT Transcribe

Model ID at review
gpt-transcribe
Provider
OpenAI
Main category
Speech
Open weights
No, available as a hosted service
Last reviewed
September 24, 2026

Fit

Where it fits and where it does not

Good at

  • Transcribing calls, meetings and voice notes.

  • Streaming transcription.

  • Keyword and language hints for names and terms.

  • Feeding text into summaries and follow-ups.

  • Replacing Whisper in existing systems.

Not the right choice for

  • Generating speech, which needs a text-to-speech model.

  • Uses that need the audio kept only on your own servers.

  • Recording people without their consent.

  • Cases that need a guaranteed perfect transcript without review.

Use cases

Business use cases we would use it for

Meeting notes

Transcribing meetings for summaries and action lists. See meeting notes and follow-ups.

Call analysis

Turning support and sales calls into text for search and review.

Voice notes

Letting field staff record notes that become structured records.

Our notes

When we would choose it

OpenAI announced gpt-transcribe in August 2026 as the current speech-to-text model. It takes audio, plus optional text hints, and returns text, with support for streaming and for keyword or language hints. It is priced per minute of audio. For live conversations, OpenAI lists a separate real-time transcription model.

The older models, including whisper-1 and the GPT-4o transcribe models, are scheduled to retire on February 26, 2027. If your system uses Whisper today, there is time to test gpt-transcribe on real recordings and switch in a planned way.

Transcription is rarely the goal on its own. The value comes from what happens next: a summary, a list of actions, an updated CRM record or a searchable archive of calls. We usually pair speech-to-text with a language model such as Claude Sonnet or GPT-6 Sol for that step.

For comparison, Deepgram offers speech-to-text with a focus on real-time voice agents. Test both on your own audio before choosing. See voice AI.

Before rolling it out, we would run a short pilot: a week of real recordings, reviewed by the people who know the calls best. Count missed names, wrong numbers and misheard terms. Those are the errors that cause problems in summaries and records later, and keyword hints or a small glossary often fix most of them.

Before you commit

Things to check before you commit

  • Migration dates

    whisper-1 and the GPT-4o transcribe models retire on February 26, 2027. Plan the move.

  • Your own audio

    Test with real recordings, accents and background noise.

  • Consent and data

    Get consent to record, and confirm data terms for audio.

  • Real time

    For live use, OpenAI lists a separate real-time transcription model.

Alternatives

Models to compare it with

Deepgram Nova-3 and Flux

Deepgram's speech-to-text models: Nova-3 for general transcription in 60+ languages, and Flux for voice agents with built-in end-of-turn detection.

GPT-4o mini TTS

OpenAI's current text-to-speech model for turning text into natural spoken audio, at a low price level.

ElevenLabs Eleven v3

ElevenLabs text-to-speech: the expressive Eleven v3 in 70+ languages, a real-time conversational version, and the low-latency Flash models, with voice cloning.

Keep exploring

FAQ

Questions people ask us

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