OpenAI: GPT-5.3-Codex

GPT-5.3-Codex is a text model from OpenAI, released on 24 February 2026. It is described by the provider as OpenAI's most advanced agentic coding model, combining the software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It sits in the text category and is aimed at developers who need a model that can handle both code-centric work and general reasoning tasks within the same endpoint.

The model accepts text, image and file inputs, and produces text output. It has a context window of 400,000 tokens and a maximum output of 128,000 tokens, which allows long source files, documentation, logs or multi-file repositories to be supplied in a single request while still leaving room for a substantial response. Because images and files are accepted as input, screenshots, diagrams, PDFs and similar artefacts can be passed alongside the prompt rather than converted to text first. Supported request parameters include include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity.

Those capabilities point to a few practical uses. Agentic coding is the primary one: tool calling and tool_choice let the model drive external functions, run commands or edit files in a loop, while the large context window keeps a working set of code in view. Reasoning controls (reasoning, reasoning_effort, include_reasoning) suit tasks that need deliberate multi-step analysis, such as debugging, refactoring or reviewing a pull request. Structured outputs and response_format are useful when the result must be parsed by another program, for example generating JSON specs, test cases or migration plans. The image and file inputs extend this to work that starts from a screenshot of an error, a design mock-up or a specification document.

You call the model through api14's OpenAI-compatible API using the model id "openai/gpt-5.3-codex". Because the interface follows the OpenAI chat completions shape, existing OpenAI SDKs and clients work with only a base URL and key change. Set the model field to that id and pass the parameters listed above as needed: tools and tool_choice for function calling, response_format or structured_outputs for machine-readable replies, reasoning_effort to trade depth against latency, max_tokens or max_completion_tokens to cap the response, and seed for more reproducible sampling. Verbosity gives an additional control over how much the model says.

The main thing to weigh is scope. Output is text only, so image or audio generation is out of reach here, and the model is positioned around coding and reasoning rather than, say, real-time or specialised non-text work. If a task does not involve code, tools or long structured inputs, a lighter text model may be a better fit. For agentic coding, long-context analysis and structured generation, this model is the appropriate choice on api14.