OpenAI: GPT-6 Sol Pro (batch)

GPT-6 Sol Pro (batch) is a text model from OpenAI, listed on api14 under the model id openai/gpt-6-sol-pro:batch. It is the same underlying model as GPT-6 Sol, served with reasoning.mode set to pro, which is intended to produce higher-quality responses on complex tasks. The provider release date is 2026-09-22. The batch variant is the version you call through api14's OpenAI-compatible endpoint.

The model accepts text, image and file inputs, and it returns text only. Its context window is 1,050,000 tokens, and the maximum output is 128,000 tokens. Because it takes images and files alongside text, you can send mixed prompts that combine written instructions with visual or document content in a single request. The large context window means long inputs, such as lengthy documents or extended conversation histories, can be handled without splitting them across calls.

Those capabilities point to a few practical uses. Long-document work is a natural fit: reviewing contracts, specifications or research material that would not fit in a smaller context. Image input supports tasks where a screenshot, diagram or scanned page needs to be read together with text instructions. File input suits pipelines that process uploaded documents and expect a text result. The reasoning controls make the model relevant for multi-step problems where you want more deliberate answers, and tool calling lets it act inside an agent loop rather than only producing standalone text.

To call it through api14, use the model id openai/gpt-6-sol-pro:batch against the OpenAI-compatible API, so existing OpenAI SDKs and clients work with a base URL change. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. In practice that means you can cap output length with max_tokens, steer reasoning depth with reasoning and reasoning_effort, request JSON or schema-conforming output with response_format and structured_outputs, control sampling reproducibility with seed, define and select tools with tools and tool_choice, and adjust answer detail with verbosity.

One thing to weigh is that pro mode spends far more than the standard mode, so it suits complex tasks rather than high-volume simple ones. If your workload is straightforward text generation, the non-pro GPT-6 Sol may be the better fit. There is also no audio or video input here, and output is text only, so tasks needing those modalities fall outside this model.