OpenAI: GPT-5.5 Pro (batch)

GPT-5.5 Pro is a text model from OpenAI, positioned as a high-capability option optimized for deep reasoning and accuracy on complex, high-stakes workloads. It sits in the text category and is offered through api14 under the model id openai/gpt-5.5-pro:batch. The batch designation indicates this variant is intended for batch style usage rather than interactive, latency-sensitive traffic, so it is worth keeping in mind when you are choosing between endpoints for a given workload.

The model accepts text, images and files as input, and returns text only. Its context window is 1,050,000 tokens, and the maximum output is 128,000 tokens. The knowledge cutoff is 2025-12-01, so anything after that date is outside what the model was trained on. The combination of a very large context window and a large output ceiling means you can supply long documents, multiple files or image inputs in a single request and still have room for a substantial generated response.

Those capabilities point to workloads where the input is large and the reasoning required is deep. Long document analysis, review of contracts or technical specifications, multi-file code or configuration review, and extraction of structured data from mixed text and image inputs are all natural fits. Because the model supports tools and structured outputs, it also suits agent-style pipelines where the model needs to call functions or return JSON that downstream code can parse. The reasoning controls let you trade off how much internal deliberation the model does against response time and output length, which matters when accuracy is more important than speed.

To call it through api14, use the OpenAI-compatible chat completions interface with the model id openai/gpt-5.5-pro:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. If you already have code written against the OpenAI SDK, you can point the base URL at api14 and change only the model string. The seed parameter is useful when you want repeatable outputs, and response_format together with structured_outputs is the route to schema-constrained responses.

There are no stated restrictions in the facts beyond the modality and parameter lists, so the main practical consideration is the batch nature of this variant. If your application needs low-latency, interactive responses, a non-batch model is likely a better fit. If your workload is offline, high volume, or tolerant of queued processing, this model gives you the full context window and reasoning controls without the constraints of a synchronous endpoint.