OpenAI: GPT-5.4 Mini (batch)

GPT-5.4 Mini (batch) is a text generation model from OpenAI, offered on api14 under the model id openai/gpt-5.4-mini:batch. It is a smaller, more efficient member of the GPT-5.4 family, built for high-throughput workloads while keeping the core capabilities of the larger GPT-5.4 model. The batch variant is intended for asynchronous, high-volume jobs rather than interactive, turn-by-turn chat.

The model accepts text, images and files as input, and produces text as output. It has a context window of 400,000 tokens and can generate up to 128,000 tokens in a single response. Its knowledge cutoff is 2025-08-31. Because it handles both text and image inputs, it can work with mixed content such as documents with embedded figures, screenshots and scanned pages alongside written instructions.

Those capabilities point to a fairly wide set of practical uses. Reasoning and coding are the areas OpenAI highlights, so code generation, code review and debugging assistance are natural fits, as are multi-step analytical tasks where the model has to work through a problem before answering. The large context window suits long document processing: summarising reports, extracting structured data from contracts, or answering questions over a whole codebase or a set of files. Image input extends this to tasks like reading charts, transcribing tables from screenshots and describing visual content. The batch orientation makes it a reasonable choice for offline pipelines, bulk classification, dataset enrichment and other jobs where requests are queued rather than awaited one at a time.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-5.4-mini:batch, so existing OpenAI SDKs and clients work with only the base URL and key changed. The request parameters it supports 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 internal reasoning the model does with reasoning_effort and reasoning, request JSON-shaped answers with response_format or structured_outputs, make sampling reproducible with seed, and give the model callable functions through tools and tool_choice. include_reasoning and verbosity let you decide how much of the reasoning trace and how much detail you want back.

One thing to weigh is the batch nature of this endpoint. If your application needs immediate responses inside a chat interface or an agent loop, a non-batch model is likely the better fit, and the same applies if you need audio or video input, since this model only takes text, images and files and only returns text. Within those bounds, though, the combination of a 400,000 token context, 128,000 token output ceiling and tool and structured output support covers most document-heavy and code-heavy workloads.