OpenAI: o4 Mini (batch)

OpenAI o4 Mini (batch) is a compact reasoning model from OpenAI's o-series, offered through api14 under the model id openai/o4-mini:batch. It belongs to the text category and is built for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. The batch variant is the same model surfaced for batch style workloads, so the reasoning behaviour you get here matches what you would expect from o4 Mini in general.

The model accepts text, images and files as input, and it returns text. It has a context window of 200,000 tokens and can produce up to 100,000 output tokens, which is generous for long reasoning traces, extended answers or large structured payloads. Its knowledge cutoff is 30 June 2024. Because it is a reasoning model, it can expose its reasoning steps when you ask for them, and it supports tool use, which makes it suitable for agentic patterns rather than plain single turn question answering alone.

Those capabilities point to a few practical uses. Multimodal input plus tool calling suits agents that need to read a screenshot or a document, decide what to do next, and call functions along the way. The large context window helps with long documents, codebases or multi turn conversations where earlier context matters. Structured outputs and response format support make it a reasonable choice for extraction, classification and other tasks where you need machine readable results. Reasoning output is useful when you want to inspect how the model reached an answer, for debugging or for tasks where the intermediate steps matter.

You call it through api14's OpenAI-compatible API using the model id openai/o4-mini:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice and tools. In practice that means you can control the reasoning behaviour and whether reasoning is returned, cap output length with max_tokens, request JSON or schema constrained output through response_format and structured_outputs, set a seed for more reproducible sampling, and define tools with tool_choice to drive function calling.

Keep the knowledge cutoff in mind: anything after 30 June 2024 is outside what the model learned, so time sensitive questions may need retrieval or tools to supply fresh facts. The model outputs text only, so if you need generated images or audio you would look elsewhere. Within those bounds it is a sensible default for reasoning heavy, multimodal and tool driven work where speed and efficiency matter.