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GPT Transcribe
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GPT Transcribe is a closed-weights speech-to-text model from OpenAI, released July 2026. Licence: proprietary.
Score within the Open ASR English short-form WER (March 2026) comparison set only.
$ how it compares
The speech-to-text models ranked either side of GPT Transcribe, out of 176 with a score, then more from OpenAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 11 |  Gemini 3.5 Transcribe | 67±6 | api $0.0050/audio min | ||
| 12 |  Scribe v2 | 66±1 | api $0.0037/audio min | ||
| 13 | GPT Transcribe | 64±4 | api $0.0045/audio min | ||
| 14 |  Zoom Scribe v1 | 64±5 | api price unknown | ||
| 15 |  Granite Speech 5.0 470M TurboCTC NC | 64±4 | local | ||
| more from OpenAI | |||||
| 48 | GPT-Realtime-Whisper | 59±3 | api $0.017/audio min | ||
| 61 | Whisper large-v3-turbo | 57±1 | api $0.0005/audio min +local | ||
| 67 | Whisper large-v3 | 57±1 | api $0.0018/audio min +local | ||
see all speech-to-text models · find the ones that fit your machine
$ what it's good at
other tests3 not in a task · 94% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| VoiceCodeBench · batch canonical token/entity match (CTEM) | 89.3% ✎ | ▄···▄··▄▄▄·█ beats 4/6 | 39% |
| VoiceCodeBench · batch WER (300 English workplace segments) | 6.1% ✎ | ▄·▄·▄·▄█···▄ beats 3/6 | 32% |
| Danish ASR Leaderboard · mean WER over 5 Danish test sets (RyeAI) | 16.7% ✎ | ▂▂···▃▂▅█▃▃▂ beats 15/16 | 22% |
$ where to run it
Through an API
api · $0.0045/audio min · developers.openai.com ↗ · price sourceUSD per input audio minute. Exact endpoint gpt-transcribe. Product: Audio transcription. API model: gpt-transcribe. Settings: endpoint=/v1/audio/transcriptions, streaming=no. Price basis: published rate. Currency: USD.
$ how the score was worked out · VoiceCodeBench (arXiv:2608.28… 71%, Danish Speech-to-Text Leaderb… 22%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 VoiceCodeBench (arXiv:2608.28916v2) · structured-token recovery ASR benchmark | 2 | 71% |
 Danish Speech-to-Text Leaderboard (RyeAI) · results dataset | 1 | 22% |
| prior · pull toward the middle | 6% | |
| weighted average | = 63.9 |
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data updated · sources and licences · json