OpenAI: o3 Mini High

Implements OpenAI's chain-of-thought reasoning architecture with high reasoning_effort setting, allocating extended computational budget to internal reasoning steps before generating responses. The model performs multi-step logical decomposition for STEM problems, explicitly working through intermediate reasoning states rather than direct answer generation. This is achieved through a configurable reasoning effort parameter that controls the depth and duration of the internal reasoning process.

Provides REST API access to the o3-mini-high model through OpenAI's standard chat completion endpoint, supporting both streaming and non-streaming response modes. Requests are authenticated via API key and transmitted over HTTPS, with responses formatted as JSON containing token usage metadata, finish reasons, and generated text. The streaming variant uses server-sent events (SSE) to deliver tokens incrementally, enabling real-time response rendering in client applications.

Balances computational cost and reasoning capability through the o3-mini architecture, which uses fewer parameters and optimized inference than o3-full while maintaining extended reasoning for STEM tasks. The high reasoning_effort setting allocates extended computation specifically to STEM reasoning patterns rather than general language understanding, reducing wasted computation on non-STEM queries. Cost is further optimized through selective reasoning — developers can use lower reasoning_effort settings for simpler queries and reserve high effort for complex problems.

Supports multi-turn conversation history where each turn can leverage extended reasoning, maintaining conversation context across multiple exchanges. The model processes the full message history (system prompt + all previous user/assistant messages) before applying reasoning_effort to generate the next response. This enables interactive problem-solving sessions where users can ask follow-up questions, request clarifications, or build on previous reasoning steps without losing context.

Supports JSON mode and schema-based output constraints through OpenAI's structured output API, allowing developers to specify a JSON schema that the model must adhere to when generating responses. The model generates valid JSON that conforms to the provided schema, with built-in validation ensuring the output matches the specified structure, types, and constraints. This is particularly useful for STEM applications where structured data extraction (equations, solutions, step-by-step breakdowns) is required.