OpenAI: GPT-5.6 Sol Pro (batch)

GPT-5.6 Sol Pro (batch) is a text model from OpenAI, listed on api14 under the model id openai/gpt-5.6-sol-pro:batch. It is the same underlying model as GPT-5.6 Sol, served with reasoning.mode set to pro so that it produces higher-quality responses on complex tasks. The provider release date is 2026-07-09, and the knowledge cutoff is 2026-02-16. The batch label indicates how this variant is served rather than a different base model.

The model accepts text, images and files as input, and it returns text only. Its context window is 1,050,000 tokens, with a maximum output of 128,000 tokens. Supported request parameters include include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. Because pro mode spends far more reasoning effort than the standard variant, responses on hard problems take longer and consume more of the reasoning budget.

Those capabilities point to a fairly specific set of uses. The very large context window suits work where a lot of material has to be considered at once, such as reviewing long documents, codebases or sets of files alongside images. Tool calling and structured outputs make it a reasonable fit for agent style workflows and for pipelines that need responses in a fixed schema. The reasoning controls let you tune how much effort the model applies, which is useful when a task is genuinely difficult rather than routine. Image and file input extend this to tasks that mix prose, screenshots, diagrams and documents.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5.6-sol-pro:batch. Requests follow the familiar chat completions shape, so existing OpenAI client libraries work with a change of base URL and model id. The parameters listed above can be passed in the request body: reasoning and reasoning_effort to control effort, include_reasoning to surface reasoning content, max_tokens to cap output, response_format and structured_outputs for formatted replies, seed for reproducibility, and tools with tool_choice for function calling. verbosity adjusts how much the model says.

The main thing to weigh is that pro mode is heavier than the standard GPT-5.6 Sol variant. If your task is simple, latency sensitive or does not benefit from deeper reasoning, the non-pro model is likely the better fit, and the batch serving mode may not suit workloads that need immediate responses. For genuinely complex work where quality matters more than speed, this model is the one to reach for.