OpenAI: GPT-5.1-Codex-Max

GPT-5.1-Codex-Max is an OpenAI model in the GPT-5.1 family, released by the provider on 4 December 2025. It is described as OpenAI's agentic coding model, built on an updated version of the 5.1 reasoning stack and trained for agentic work. It sits in the text category and is aimed at software development rather than general conversation, so it is best understood as a specialist tool for code and the tasks around it.

The model accepts text and images as input and returns text only. Its context window is 400,000 tokens, which is large enough to hold substantial source trees, long logs or extended agent transcripts in a single request, and it can produce up to 128,000 output tokens. Because it is a reasoning model, it can expose its reasoning through the include_reasoning and reasoning parameters, and reasoning_effort lets you trade depth of thinking against latency. Verbosity controls how much the model writes, and structured_outputs plus response_format let you constrain replies to a schema when you need machine-readable results.

Those capabilities point to a fairly specific set of uses. Long-running coding agents that read a repository, plan changes and edit files across many steps are the primary fit, especially when the working context is too large for smaller windows. Code review and refactoring of large files, migration work, test generation and debugging from stack traces all benefit from the combination of a big context and reasoning. Image input extends this to tasks where a screenshot, diagram or UI mockup needs to be interpreted alongside code, such as turning a design into markup or diagnosing a rendering problem from a captured screen.

You call the model through api14's OpenAI-compatible API using the model id "openai/gpt-5.1-codex-max". Requests follow the familiar chat completions shape, so existing OpenAI SDKs and clients work with only a base URL and key change. The supported parameters are include_reasoning, max_completion_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. Tool calling is available through tools and tool_choice, which is what makes multi-step agent loops practical, and seed helps when you want more repeatable sampling.

Keep the scope in mind when choosing it. Output is text only, so it will not generate images or audio, and the reasoning controls mean responses can take longer than a plain chat model when reasoning_effort is set high. For short, low-stakes exchanges or simple text generation where none of the coding or reasoning behaviour matters, a lighter model will usually be the more sensible pick.