OpenAI: GPT-5.1 (batch)

OpenAI: GPT-5.1 (batch) is a text model from OpenAI, served through api14 under the model id openai/gpt-5.1:batch. It belongs to the GPT-5 series, and OpenAI describes GPT-5.1 as the latest frontier-grade model in that series, with stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. The batch variant is the version you call through this endpoint, and the provider release date is 13 November 2025.

The model accepts text, images, and files as input, and it returns text. Its context window is 400,000 tokens, and the maximum output is 128,000 tokens, so it can take in large documents, long conversation histories, or a mix of written material and images in a single request, and still produce a substantial response. GPT-5.1 uses adaptive reasoning, which means the amount of internal reasoning it applies can vary with the task rather than being fixed.

Those capabilities point to a fairly wide set of practical uses. Long-context work such as reviewing large codebases, contracts, research papers, or support transcripts fits the 400,000 token window, and the file and image inputs make it usable for tasks that mix screenshots, scanned pages, or diagrams with text instructions. Because instruction adherence is a stated focus of GPT-5.1, it suits structured extraction, classification, and multi-step workflows where the output has to follow a defined shape. The reasoning controls also make it a reasonable choice for problems that benefit from deliberate analysis, such as debugging, planning, or evaluating competing options, while the batch setting is aimed at workloads where requests are submitted together rather than one at a time.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5.1:batch, so existing OpenAI client libraries and request formats work without changes. 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, control how much reasoning effort the model spends, request structured or JSON-shaped output through response_format and structured_outputs, set a seed for more reproducible sampling, and define function calling with tools and tool_choice. The verbosity parameter lets you steer how long or short the answers are, and include_reasoning lets you decide whether reasoning content is returned alongside the final text.

There is no information here about pricing, rate limits, or latency, so check the live price table and your account limits before committing to a high-volume workload. Since the model outputs text only, tasks that require generated images, audio, or video need a different model. If your workload is small, latency-sensitive, or does not need the long context window or the reasoning controls, a lighter text model may be a better fit, but for large inputs, mixed text and image or file inputs, and requests that need structured output or tool use, GPT-5.1 (batch) covers those cases directly.