o3-mini is a small, high-performance reasoning model optimized for STEM tasks — math, science, and coding — that delivers fast responses at low cost. It combines advanced step-by-step reasoning with developer-friendly features so teams can build reliable, production-ready automation. Users can set reasoning effort (low, medium, high) to trade off latency and depth of analysis depending on the task: quick verification or deep multi-step proofs and PhD-level problem solving. o3-mini produces structured, machine-readable outputs via JSON Schema constraints, making it simple to integrate into automated pipelines and downstream systems. The model supports function calling and tool use, enabling safe automation such as invoking external code, running searches, reading files, and interacting with APIs. Streaming output is available for real-time interfaces. For multimodal workflows, o3-mini accepts image inputs for document question answering and receipt interpretation — though it is not intended as a dedicated object-detection vision model. Compared with its predecessor, it achieves faster time-to-first-token and lower latency while improving accuracy on technical benchmarks, so you get better reasoning at lower cost. Practical uses include automated grading and tutoring for STEM education, coding assistance and competitive programming support, enterprise automation that requires exact structured outputs, and scientific research assistance. Limitations: it’s focused on technical reasoning rather than open-ended creative dialogue and is not optimized for high-speed vision tasks like object detection. Overall, o3-mini is ideal for developers and organizations that need a compact, economical model that reliably solves complex technical problems and integrates seamlessly into production workflows.