OpenAI: GPT-5.2 Pro (batch)

GPT-5.2 Pro is a text model from OpenAI, listed on api14 as openai/gpt-5.2-pro:batch. It sits in the text category and is described by the provider as OpenAI's most advanced model, with major improvements in agentic coding and long context performance over GPT-5 Pro. The provider release date is 2025-12-10. The batch suffix on the model id indicates the batch variant of this model.

The model accepts image, text and file inputs, and it returns text only. Its context window is 400,000 tokens, and the maximum output is 128,000 tokens. It is optimized for complex tasks that require step-by-step reasoning, and it exposes reasoning controls through its supported parameters, so you can tune how much internal reasoning effort the model applies and whether reasoning content is included in the response.

Because of the large context window and the reasoning focus, the model fits work where a lot of material has to be considered at once: long documents, large codebases and multi file repositories, and agentic coding tasks that involve planning and executing several steps. File and image input extend this to tasks that mix prose, code and visual material, such as reviewing a specification alongside diagrams, or extracting structured data from scanned documents. Structured outputs and tool calling make it usable as the reasoning core of an automated pipeline rather than only as a chat endpoint.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5.2-pro:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. In practice this means you can cap output length with max_tokens, control reasoning depth with reasoning and reasoning_effort, request machine readable replies with response_format or structured_outputs, define callable functions with tools and tool_choice, and adjust answer style with verbosity. The seed parameter allows you to make sampling more reproducible where that matters.

Two practical points follow from the facts. Output is text only, so if your application needs generated images or audio, this model is not the right fit. The batch variant is also worth checking against your latency needs, since batch style endpoints typically trade immediacy for throughput; if you need interactive response times, confirm the behaviour of this variant before committing to it. Otherwise, for long context reasoning, agentic coding and structured extraction, the combination of a 400,000 token context window, a 128,000 token output ceiling and tool support covers a wide range of production workloads.