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Model details

GPT-4o Transcribe (OpenAI)

GPT-4o Transcribe is an OpenAI automatic speech recognition model designed to convert spoken audio into written text. It belongs to the broader gpt family and is presented as a speech-to-text endpoint that builds on the GPT-4o architecture, positioning it as a modern successor for transcription workloads. The model is intended for developers who need reliable transcription of audio recordings, voice notes, or streamed speech into clean text output suitable for downstream processing, search, summarization, or archival.

Compared with the original Whisper line, GPT-4o Transcribe is described as offering improved word error rate and better language recognition, reflecting how OpenAI has folded newer language modeling capabilities into its transcription stack. This lineage matters in practice: audio is decoded through a strong generative backbone rather than a purely acoustic pipeline, which tends to help with noisy audio, accented speech, and mixed-language input. For teams evaluating speech-to-text options, the model fits naturally alongside other OpenAI gpt family endpoints as a specialized transcription variant that pairs well with chat or analysis models for end-to-end voice workflows.

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LLM Gateway
Model key
openai/gpt-4o-transcribe
Release date
Mar 20, 2025
Last updated
Mar 20, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$2.50
Output token cost
$10.00

Limits

Output tokens
2,000 tokens
Context window
16,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GPT-4o Transcribe (OpenAI)

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Coverage

A July 2026 analysis reports that OpenAI has split its speech-to-text stack into workload-specific paths, with GPT-4o Transcribe positioned as the documented model for completed, uploaded audio recordings. The article explicitly states that OpenAI describes GPT-4o Transcribe as a GPT-4o-based speech-to-text model with The same piece notes that this split is consequential beyond naming: live transcription requires session management, buffering, turn detection, reconnection logic, and careful handling of transcript revisions, while file-based workflows need queues, durable job states, retries, speaker labels, timestamps, and predictab

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