OpenAI o4-mini is a compact reasoning model in the o-series family, designed to think through complex problems before responding rather than generating immediate answers. Unlike earlier models that produce quick responses, o4-mini is trained to deliberate, using tools within its chain of thought to crop or transform images, search the web, run Python analysis, and evaluate uploaded files while solving multi-step challenges. The model can agentically combine capabilities like web browsing, file search, image generation, and structured output to execute tasks independently on a user's behalf. This design makes it particularly effective at tackling complex math, coding, and scientific challenges while maintaining strong visual perception and analysis skills.

The o4-mini builds on its predecessor o3-mini with refined reinforcement learning training that yields competitive performance across benchmarks including AIME and SWE-bench, approaching the capabilities of the larger o3 model in several domains. Its efficient architecture enables high accuracy in STEM tasks and visual problem solving while keeping latency and operational costs low, making it well-suited for high-throughput production scenarios. The model represents a step toward more agentic AI by integrating tool use throughout the reasoning process, allowing it to chain multiple capabilities and deliver thoughtful, formatted answers typically in under a minute.