OpenAI: GPT Audio

GPT Audio is an OpenAI model listed on api14 under the identifier openai/gpt-audio. It sits in the audio category and is OpenAI's first generally available audio model. The snapshot includes an upgraded decoder intended to produce more natural sounding voices, and it maintains better voice consistency than earlier audio work from the same provider. If you are building anything that needs to speak or listen rather than only read and write text, this is the model OpenAI positions for that job.

The model accepts both text and audio as input, and it can return both text and audio as output. That two way capability is the main thing that separates it from a pure text model: you can send audio in and get a transcript or a reply out, or send text in and get synthesized speech back. The context window is 128000 tokens, which is large enough to hold long conversations or lengthy audio transcripts alongside instructions and prior turns. The maximum output is 16384 tokens, so responses, including any generated audio content, are bounded by that figure.

Practical uses follow directly from those modalities. Voice assistants and conversational agents are the obvious fit, since the model can take spoken input and answer in speech while keeping a consistent voice across turns. Transcription and audio understanding tasks work too, including summarising a recording or answering questions about what was said. Because it also handles text output, you can use it for chat style applications where some users type and others talk, without running two separate pipelines. The upgraded decoder matters most where voice quality and consistency are noticeable to end users, such as customer facing agents or narrated content.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-audio, so existing OpenAI SDKs and clients work with only a base URL and key change. The supported request parameters are frequency_penalty, logit_bias, logprobs, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. Tool calling and structured outputs are available, which means you can wire the model into function calling flows or constrain its text output to a schema. The seed parameter helps when you want repeatable results, and response_format lets you steer the shape of the reply.

Keep the output ceiling in mind: 16384 tokens is generous for conversation but not unlimited, so very long generated audio or extended documents may need to be split across calls. If your task is purely text and never touches audio, a dedicated text model may be a simpler and more predictable choice, since the audio capabilities here are what justify reaching for this model. For anything that genuinely needs speech in or speech out, GPT Audio is the direct option on api14.