OpenAI: GPT-5 Pro

GPT-5 Pro is a text generation model from OpenAI, listed on api14 under the model id openai/gpt-5-pro. It sits in the text category and is described by OpenAI as its most advanced model, with improvements in reasoning, code quality, and user experience. It was released by the provider on 2025-10-06, and its training knowledge runs up to a cutoff of 2024-09-30, so anything after that date is outside what the model learned during training.

The model accepts text, images, and files as input, and it returns text only. It has a context window of 400,000 tokens, which is large enough to hold long documents, extended conversations, or sizeable codebases in a single request, and it can produce up to 128,000 tokens in one response. Because it takes images and files as well as text, you can send a mix of formats in the same call rather than converting everything to plain text first.

Those capabilities point to work where the input is long and the reasoning is multi step. Typical fits include analysing lengthy contracts or reports alongside their supporting files, reviewing large code changes and explaining what they do, answering questions over a document set you supply directly in the prompt, and extracting structured data from mixed text and image inputs. The reasoning controls make it a reasonable choice for tasks where you want the model to work through a problem before answering, such as planning, debugging, or multi constraint instruction following.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5-pro, so existing OpenAI SDKs and clients work with only the base URL and key changed. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools, and verbosity. In practice that means you can cap output length with max_tokens, tune how much reasoning effort the model spends, request JSON shaped output through response_format or structured_outputs, and give the model callable functions with tools and tool_choice. The seed parameter allows repeatable sampling where determinism matters, and verbosity lets you steer how long or short the answers are.

A few practical notes follow from the facts above. Output is text only, so if you need generated images or audio, this model is not the right fit. The knowledge cutoff of 2024-09-30 means questions about later events depend on what you put in the prompt rather than on the model's own knowledge. And with a 400,000 token context and up to 128,000 output tokens, very large requests are possible, so it is worth checking the live price table on this page before sending high volume traffic through it.