gpt-transcribe is an OpenAI transcription service for converting audio to text, suitable for organizing interviews, courses, meetings, and spoken content into text assets. It works with audio files and delivers recognized text, rather than generating speech or handling general chat. When using it, you can structure requests with language information and text prompts, then connect transcription results to search, editing, or summarization workflows.
Clarify capacity, inputs and outputs, and invocation methods before choosing a model.
The above describes the input, output, and configuration scope of the service endpoint and does not represent standalone native version specifications. Extended options such as subtitles and timestamps must be validated for each task.
Learn what gpt-transcribe can bring to your work.
The core purpose of gpt-transcribe is to convert spoken content in recordings into text, bringing audio into a searchable, copyable, and editable workflow. Applications can read the text field in JSON and save it as interview transcripts, course note materials, or meeting record drafts; summarization, categorization, and insight extraction are better suited as subsequent processing steps.
When processing recordings containing industry terminology, product names, or specific languages, use the input language and prompt fields to provide context. It is recommended to first compile a concise glossary and contextual description, then test the results with representative clips. These settings support the transcription task and should not be treated as mandatory replacement rules, nor can they replace manual proofreading of key names.
Basic text is suitable for archiving and retrieval; when it needs to enter a video editing workflow, design the deliverable around the subtitle formats and timestamp options provided by the input. First confirm that the selected configuration can return the required structure, then process the complete material. Transcribed text, subtitle segmentation, and video synchronization are separate stages, so obtaining text should not be directly equated with completing subtitle production.
Start with specific tasks to find where the model can be effective.
Input interview recordings and prepare the interviewee's name, organization name, and topic terminology to obtain an editable text draft. Editors can use it to search for original statements, mark quoted passages, and organize the line of questioning; before formal publication, listen again to segments involving numbers, proper nouns, and key statements, preserving the interviewee's original meaning rather than relying on automatic rewriting.
Transcribe course or lecture audio into text, saved by course name, topic, and recording batch, making it easier for learners to search concepts and review content. Deliverables can include body text materials and a search index; chapter divisions, knowledge-point summaries, and exercises need to be organized separately, and a single transcription request should not be treated as a complete course content generation workflow.
Transcribe accompanying video recordings or narration materials to first obtain a text draft, then proceed to proofreading, sentence segmentation, and subtitle editing. When subtitle files are needed, first test the format and time alignment with a short clip, then process in batches. Background music, overlapping speech from multiple speakers, and editing points should be reviewed carefully to avoid publishing recognized text directly without inspection.
Choose based on task complexity, input materials, and expected results.
When you need to convert existing recordings into text, you can choose gpt-transcribe. It and whisper-1 appear as separate transcription options in the client. When selecting, use the same batch of recordings to compare terminology recognition, text readability, and output configuration compatibility. Do not judge speed or accuracy differences based on the name alone, and do not directly treat it as a version of gpt-4o-transcribe.
If the deliverable is a recording transcript, prioritize a transcription workflow. If you need to extract action items from the transcript, write a summary, or answer content-related questions, you can add a text-processing step after transcription. If the goal is to read text aloud, choose a text-to-speech service. Separating tasks this way lets you independently check recognition errors and content-processing results, making it easier to identify issues.
From a small-scale task to full integration.
Clarify the goal, required inputs, and output requirements, using real business samples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Understand output quality and capability boundaries before production use.
Answers to common questions about using gpt-transcribe.
When using it, treat gpt-transcribe as a standalone invocation ID. Do not replace it with gpt-4o-transcribe or gpt-4o-mini-transcribe on your own. Similar names do not mean identical versions; when choosing, focus more on your own recording samples and required delivery format.
Submit a binary file to /v1/audio/transcriptions, and explicitly specify model=gpt-transcribe. Do not rely on the default selection when the model is omitted. If submitting a URL through an MCP audio transcription tool, follow that tool's input method; do not treat the URL string directly as an uploaded file.
The endpoint provides srt and vtt request options. You can first test a short recording to see whether it produces subtitle results that meet your needs. Before final delivery, also check sentence segmentation, time alignment, and proper nouns; if your workflow mainly uses plain text, you can transcribe first and then arrange the timeline during subtitle editing.
You can prepare concise language information and terminology context around language, prompt, or keywords[], and first test common names and easily confused terms. Prompts are not dictionaries that guarantee correct recognition, nor are they suitable for including rewriting instructions; key terms should still be checked item by item against the original recording.
gpt-transcribe's primary workflow is audio-file transcription, and its output is recognized text rather than conversational responses. Even when using streaming configuration, it should not be considered equivalent to a real-time two-way voice conversation; to listen and respond while speaking, you also need to design separate audio capture, conversation processing, and speech playback stages.
Model information · Updated: 2026-10-01. For invocation parameters and billing rules, see the API and pricing sections.
Start with clear goals, and use real-world results to determine whether it suits your work.
Pricing
$0.0001· per second
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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