OpenAI: o4 Mini High

OpenAI: o4 Mini High is a text model from OpenAI, released on 16 April 2025. It belongs to the o-series of reasoning models, and it is the same model as o4-mini with the reasoning_effort parameter set to high. The "high" variant therefore represents the more deliberate end of that model's reasoning range, while keeping the compact, efficiency-oriented design of o4-mini.

The model accepts image, text and file inputs, and it produces text output. It has a context window of 200,000 tokens and a maximum output of 100,000 tokens, with a knowledge cutoff of 30 June 2024. Its supported request parameters include include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice and tools. The presence of tools and tool_choice means it can be used in function calling and agent style workflows, while response_format and structured_outputs let you constrain the shape of the returned text.

Those capabilities point to a fairly specific set of use cases. Because it reasons before answering and can accept files and images alongside text, it suits tasks where the model must work through a problem rather than pattern match an answer: multi step analysis of documents, interpreting charts or screenshots together with written context, and code or logic problems that benefit from a careful chain of thought. The tool parameters make it a reasonable fit for agentic pipelines that call external functions, and structured_outputs is useful when the result has to be parsed by another program. The 200,000 token context window allows large inputs, such as long reports or several files, to be handled in a single request.

You call the model through api14's OpenAI-compatible API using the model id "openai/o4-mini-high". Because the interface follows the OpenAI chat completions format, existing OpenAI client libraries and SDKs work with only a base URL and key change. In the request you can set the parameters listed above, including reasoning_effort to control how much reasoning the model applies, max_tokens to cap the output, and tools or tool_choice when you want function calling. The include_reasoning parameter lets you decide whether reasoning content is returned with the response.

One practical consideration is that this variant is defined by high reasoning effort, so it is oriented toward harder problems rather than quick, low latency exchanges. For simple or high volume text generation where deep reasoning adds little, the base o4-mini or another text model may be a better fit. The model outputs text only, so tasks that require generated images or audio fall outside its scope.