OpenAI: GPT-5.4 Pro (batch)

GPT-5.4 Pro (batch) is a text generation model from OpenAI, offered through api14 under the model id openai/gpt-5.4-pro:batch. It sits at the top of OpenAI's GPT-5.4 line, which the provider describes as a unified architecture, with the Pro variant tuned for enhanced reasoning on complex, high-stakes tasks. The batch label indicates this endpoint is intended for asynchronous, high-volume work rather than interactive chat.

On the input side the model accepts text, images and files, and it produces text only. The context window is 1,050,000 tokens, and the maximum output is 128,000 tokens. That combination means you can supply very large source materials, such as long documents, codebases, transcripts or image sets, and still leave room for a substantial generated answer in a single request.

The practical use cases follow from those numbers and from the reasoning focus. Long-context analysis is the obvious one: reviewing contracts, research papers or regulatory filings where the answer depends on details scattered across hundreds of pages. Document and image inputs together suit workflows that mix scanned pages, diagrams and written text, such as technical due diligence or claims processing. Because the model supports tools and structured outputs, it also fits pipelines that need to call functions, query internal systems or return machine-readable results rather than prose. The batch endpoint is a natural fit for jobs that do not need an immediate reply, for example overnight classification, summarisation or extraction across a large corpus.

Calling it through api14 works the same way as any other OpenAI-compatible model. Point your existing OpenAI client at the api14 base URL, send your request to the chat completions endpoint, and set the model field to openai/gpt-5.4-pro:batch. The request parameters supported are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools and verbosity. In practice, reasoning_effort and verbosity let you trade depth against output length, response_format and structured_outputs control whether the reply is free text or a schema-conforming object, and tools with tool_choice enable function calling. The seed parameter helps when you want repeatable sampling.

The main thing to weigh is latency and throughput rather than capability. A batch endpoint is not the right choice when a user is waiting on the response in a chat interface, and the very large context window is unnecessary for short prompts where a smaller model would return results sooner. If your workload is interactive, or if your inputs are a few paragraphs rather than a few hundred pages, a non-batch model is likely a better fit. For offline, reasoning-heavy jobs over long inputs, this model is built for exactly that shape of work.