OpenAI: GPT-4o Transcribe

GPT-4o Transcribe is a speech-to-text model from OpenAI, built on the audio capabilities of GPT-4o. It sits in the transcription category, so its job is converting spoken audio into written text rather than generating conversational replies. If you already route other OpenAI models through your stack, this one comes from the same provider and follows the same general access pattern.

The model takes audio as its only input and returns a transcription as its output. It has a context length of 128,000 tokens and a maximum output of 115,200 tokens, which is generous for transcription work: long recordings, multi-speaker sessions or large batches of audio can be handled without splitting them into many small requests. Because the output is a transcription rather than a chat completion, you should expect text that reflects what was said in the audio, not an assistant-style answer. The provider describes it as a high-quality speech-to-text model, and billing is per token on both input and output, which gives you token-level transparency into what each request consumes.

Practical use cases follow directly from that shape. Meeting and call recordings can be turned into text for notes, search or archival. Voice memos, interviews and research recordings can be transcribed for later analysis. Podcasts, lectures and similar long-form audio fit the large context window, so you can send substantial files in a single request instead of chunking them. Teams that need transcripts as a pipeline step, feeding downstream summarisation, indexing or review tools, can use the model as the first stage. Anywhere you have audio and need reliable text out of it, this is the intended workload.

You call it through api14's OpenAI-compatible API using the model id "openai/gpt-4o-transcribe". Because the API is OpenAI-compatible, you can point an existing OpenAI client at api14's endpoint and send audio input in the usual way, with no bespoke integration required. The model's supported parameters list is empty, so there are no additional tuning options to set beyond the standard request fields; keep your requests simple and rely on the model's defaults. Check the live price table on this page for current rates, since billing is per token for both input and output.

One thing to keep in mind is scope. This model is built for transcription only, so if you need an assistant that reasons over text, answers questions or produces structured output, a general chat model will fit better. Likewise, if your audio is short and simple, a lighter transcription option may be enough. For long recordings and workflows where token-level billing visibility matters, GPT-4o Transcribe is the straightforward choice.