OpenAI: GPT-5.6 Sol (batch)

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series, released by OpenAI on 9 July 2026. It sits at the top of that series and is positioned for demanding work rather than lightweight chat. The model id on api14 is openai/gpt-5.6-sol:batch, and it is listed under the text category. Its knowledge cutoff is 16 February 2026, so anything that changed 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. That combination makes it useful when a request needs to reason over a screenshot, a document or a source file alongside written instructions. The context window is 1,050,000 tokens, which is large enough to hold very long inputs in a single request, and the maximum output is 128,000 tokens. Supported parameters include max_tokens, response_format, structured_outputs, seed, tool_choice, tools, verbosity, include_reasoning, reasoning and reasoning_effort, so you can control both the shape of the response and how much reasoning effort the model spends.

Those capabilities point to a few practical uses. Complex reasoning tasks, coding work and agentic workflows are the stated strengths, with particular emphasis on command-line and multi-step coding tasks. In practice that means multi-file refactors, debugging sessions where the model has to follow a chain of steps, tool-driven agents that call functions and act on the results, and long-document analysis where the whole input fits in one context window. Structured outputs and response_format are useful when a downstream system needs predictable JSON rather than free text, and the seed parameter helps when you want repeatable sampling.

Calling it through api14 works the same way as any other OpenAI-compatible model. Send a request to the chat completions endpoint with the model field set to openai/gpt-5.6-sol:batch, and pass your inputs as messages. Image and file inputs go in the message content alongside text. You can set max_tokens to cap the response, use tools and tool_choice to expose functions, use response_format or structured_outputs to constrain the output, and tune reasoning_effort and verbosity to trade depth against brevity. The include_reasoning and reasoning parameters let you decide whether reasoning content is surfaced.

One thing to keep in mind is the batch suffix in the model id, which distinguishes this variant from other entries in the same series. If your task is simple text generation, a smaller model in the GPT-5.6 family may be a better fit, since the context window and reasoning controls here are aimed at heavier workloads. The 128,000 token output ceiling is generous but still finite, so very large generated artifacts may need to be produced in stages.