OpenAI: GPT-5.1

GPT-5.1 is a frontier-grade text model from OpenAI and the latest entry in the GPT-5 series. It was released by the provider on 13 November 2025. Compared with GPT-5, OpenAI describes it as offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style, built around adaptive reasoning. It sits in the text category and is aimed at general-purpose work rather than a narrow single task.

On the input side, the model accepts text, images, and files, so you can mix prose prompts with visual or document content in the same request. Output is text only. The context window is 400,000 tokens, which is large enough to hold long documents, extended conversation histories, or sizeable codebases in a single call. The maximum output is 128,000 tokens, so responses can be long when the task calls for it. Adaptive reasoning means the model adjusts how much internal reasoning it applies rather than using a fixed amount on every request.

Those capabilities point to a fairly wide set of uses. Long-context work such as summarising or querying large documents, contracts, or technical manuals fits the 400,000 token window. Because images and files are accepted as input, you can build workflows that reason over screenshots, scanned pages, diagrams, or attached data alongside a text prompt. Improved instruction adherence and a more natural conversational style suit chat assistants, drafting, editing, and multi-step agent-style tasks where the model has to follow a detailed brief consistently. The large output ceiling helps for generating long reports, structured documents, or extended code in one response.

You call the model through api14's OpenAI-compatible API using the model id "openai/gpt-5.1". Because the interface follows the OpenAI format, existing OpenAI SDKs and clients work with only a base URL and key change. The request parameters supported are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools, and verbosity. In practice that means you can control reasoning depth with reasoning and reasoning_effort, cap output length with max_completion_tokens or max_tokens, request JSON-shaped replies with response_format or structured_outputs, and wire up function calling with tools and tool_choice. The seed parameter allows reproducible sampling where the backend supports it, and verbosity tunes how expansive the answers are.

One thing to weigh is that output is text only, so if you need generated images or audio you would look at a different model. The reasoning controls also add a layer of configuration that simpler models do not expose, which is useful when you want to trade latency against depth but is extra surface area to manage. For straightforward, high-volume text tasks where none of the long context, multimodal input, or reasoning controls matter, a smaller model may be a simpler fit.