OpenAI: GPT-5.2-Codex

GPT-5.2-Codex is an OpenAI model in the GPT-5.2 family, released by the provider on 14 January 2026. It is an upgraded version of GPT-5.1-Codex, optimized for software engineering and coding workflows. It is designed to serve both interactive development sessions and long, independent execution of complex engineering tasks. It sits in the text category, and its focus is code rather than general conversation, which shapes the rest of what it does well.

The model accepts text and images as input and returns text as output. Its context window is 400,000 tokens, and the maximum output is 128,000 tokens. Image input is useful when the material you are reasoning about is a screenshot, a diagram, or a rendering of an interface rather than source text. The combination of a large context window and a large output budget means you can hand it an entire repository slice, a long specification, or a lengthy stack trace and still get a substantial answer back in one call.

Those capabilities point at a fairly specific set of uses. Long, independent execution of complex engineering tasks makes it a candidate for multi-step work such as reading a codebase, planning a change, and carrying it out across many files. Interactive sessions suit it to pair programming style workflows, refactoring, test generation, and debugging from a trace or a screenshot of a failing build. Structured outputs and tool support make it usable as the reasoning core of an agent that reads files, runs commands, or calls other services. Image input extends this to tasks where the relevant artifact is visual, for example turning a design mockup or a diagram into code.

You call it through api14's OpenAI-compatible API using the model id "openai/gpt-5.2-codex", so existing OpenAI SDKs and clients work with only a base URL and key change. The supported request parameters are include_reasoning, max_completion_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools, and verbosity. In practice, reasoning_effort and verbosity let you trade deliberation and answer length against latency, max_completion_tokens caps the output, response_format and structured_outputs constrain the reply shape, and tools with tool_choice wire the model into function calling. Seed helps when you want repeatable sampling.

Because it is tuned for coding, a general purpose chat model may fit better for tasks with no engineering component, such as marketing copy or open ended conversation. Response quality and latency also depend on how you set reasoning_effort and verbosity, so it is worth benchmarking those settings against your own workloads before committing. There is no stated restriction on the input modalities or parameters beyond what is listed here.