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OpenAI’s Whisper API handles completed audio files in two ways: submit audio to /v1/audio/transcriptions to transcribe it in its original language, or to /v1/audio/translations to translate it into English. For ordinary recorded speech in its original language, OpenAI’s current guide recommends starting with gpt-transcribe; whisper-1 remains useful for English translation, word timestamps, and subtitle formats.

Choose the right audio workflow and model

The key decision is what you need from the audio: text in the language spoken, English translation, or transcription of audio that is still arriving live. OpenAI’s speech-to-text guide recommends gpt-transcribe for ordinary recorded speech in its original language. Choose whisper-1 when you need English translation, word or segment timestamps, or subtitle output.

Need Endpoint or workflow Model or result
Transcribe a completed recording in its original language /v1/audio/transcriptions Start with gpt-transcribe; use whisper-1 if you need its timestamp or subtitle options.
Translate a completed recording into English /v1/audio/translations whisper-1; this endpoint translates into English only.
Transcribe audio as it arrives from a microphone, call, or stream Realtime transcription workflow Use the Realtime transcription guidance, not a completed-file request.

The file endpoint can return partial text while it processes a completed recording; that does not make it a Realtime session. The current documentation does not establish a fresh, side-by-side accuracy benchmark for these models, so choose by workflow and output requirements rather than assuming one is universally more accurate.

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Submit a completed audio file

Send the audio file and selected model to the transcription endpoint. The following Python example uses the OpenAI SDK and returns text for an original-language transcription:

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from openai import OpenAI

client = OpenAI()

with open("meeting.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="gpt-transcribe",
        file=audio_file,
    )

print(transcript.text)

To use Whisper instead, set model="whisper-1". To translate a supported completed recording into English, use the translation method with whisper-1:

with open("interview.mp3", "rb") as audio_file:
    translation = client.audio.translations.create(
        model="whisper-1",
        file=audio_file,
    )

print(translation.text)

These examples use the SDK’s file-upload interface; consult the Audio API reference for request fields and response formats. The transcription endpoint preserves the spoken language, while translation produces English text; it is not an arbitrary-target-language translation endpoint.

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Meet the file and format limits

OpenAI’s current speech-to-text guide lists a maximum upload size of 25 MB and supports mp3, mp4, mpeg, mpga, m4a, wav, and webm. The API reference lists additional formats, including flac and ogg, for the translation request field; the guide’s narrower list is the safer unified list to use when planning uploads.

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For a recording larger than 25 MB, compress it or split it into files no larger than 25 MB. Keep splits at natural pauses where possible: cutting in the middle of a sentence can remove context the model needs to recognize what was said.

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Request timestamps or subtitle files

Whisper supports timestamped output and subtitle formats. According to the Audio API reference, request verbose_json to use timestamp_granularities[]; the supported granularities are word and segment. Word-level timestamps add latency, so request them only when your application needs that precision.

For subtitle output, set response_format to srt or vtt. The API reference also lists JSON, text, and verbose JSON response formats for translation. Select the format that matches the next step in your pipeline: plain text for a transcript, timestamps for synchronized editing, or SRT/VTT for subtitle workflows.

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Guide recognition with a prompt

A prompt can supply names, acronyms, or specialized vocabulary likely to appear in a recording. Whisper’s prompt limit is 224 tokens, and OpenAI describes its prompting as offering less control than the recommended transcription model. Treat a prompt as context for recognition, not as a way to provide extensive instructions or guarantee a particular transcript.

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Compare current per-minute prices

OpenAI’s pages list these usage prices as accessed in 2026. They can change, so verify the Whisper model page and pricing page before estimating a current bill.

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Model Listed price per audio minute Relevant fit
whisper-1 $0.006 — OpenAI model page, accessed 2026 English translation, timestamps, and subtitle formats.
gpt-transcribe $0.0045 — OpenAI pricing page, accessed 2026 Recommended starting point for recorded speech in its original language.
gpt-4o-transcribe $0.006 — OpenAI pricing page, accessed 2026 An alternative transcription model; the cited price does not establish a quality ranking.
gpt-4o-mini-transcribe $0.003 — OpenAI pricing page, accessed 2026 A lower listed per-minute price; the cited price does not establish a quality ranking.

These are listed per-minute usage prices, not a comparison of transcript quality. Choose based on the needed language behavior, live versus completed audio, timestamp or subtitle requirements, and file-handling constraints.

Set expectations for language coverage

OpenAI says Whisper supports 98 languages, while cautioning that accuracy varies by language. That coverage figure does not mean performance is equal across languages or recording conditions; the current official material does not provide a language-by-language production accuracy statistic.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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