OpenAI: GPT-6 Sol (batch)

GPT-6 Sol is a text model from OpenAI and sits in the middle of the GPT-6 series. OpenAI positions it as the cost-efficient high-end option, below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. It is aimed at demanding professional work rather than quick throwaway queries.

The model accepts text, images and files as input, and it returns text. Its context window is 1,050,000 tokens, which is large enough to hold entire codebases, long document sets or extended conversation histories in a single request. The maximum output is 128,000 tokens, so responses can be long when the task calls for it. Supported parameters include max_tokens, response_format, structured_outputs, seed, tools, tool_choice, reasoning, reasoning_effort, include_reasoning and verbosity, which means you can control how much reasoning the model does, constrain its output to a schema, and let it call tools.

Those capabilities point to a few practical patterns. A large context plus image and file input suits document analysis, such as reviewing contracts, research papers or scanned reports alongside their figures. The tool and structured output support fits agent style workflows where the model plans steps, calls functions and returns JSON that your code can parse. Reasoning controls are useful for problems that need deliberation, like multi step analysis or debugging, while verbosity lets you keep answers short when you only need the conclusion. The long output limit helps for tasks like generating detailed reports or large code changes in one pass.

You call the model through api14's OpenAI-compatible API using the model id "openai/gpt-6-sol:batch". Because the interface follows the OpenAI format, existing OpenAI SDKs and client libraries work with only a change of base URL and API key. In the request you can set max_tokens to cap the response length, use response_format or structured_outputs to request JSON or a schema, and pass tools and tool_choice to enable function calling. The reasoning and reasoning_effort parameters let you trade deliberation for speed, include_reasoning controls whether reasoning content is returned, seed supports reproducible sampling, and verbosity adjusts how detailed the answer is.

The batch designation in the model id suggests this variant is intended for batch style or high throughput usage rather than latency sensitive interactive chat. If your workload needs the fastest possible turnaround, the GPT-6 Luna tier described by OpenAI may be a better fit, and if you need the strongest overall capability, the flagship GPT-6 Astra sits above this model. Otherwise, GPT-6 Sol covers demanding text tasks with image and file input at the high end of the series without going to the flagship tier.