OpenAI o1 is a reasoning model built to prioritize deep problem-solving over instant responses. Unlike models that generate answers at speed, o1 takes time to think through complex tasks step by step, using an internal chain of thought process before committing to a response. This design philosophy makes it particularly strong at multi-step logic, advanced mathematics, and coding challenges where accuracy matters more than throughput. The model was developed as a successor to the o1-preview and represents a deliberate shift away from the fast, broad-capability approach of earlier generations toward deliberate, verifiable reasoning on hard problems.

The model was trained using reinforcement learning, which taught it to break down problems, evaluate multiple solution paths, backtrack when needed, and verify its own work before responding. In testing, o1 scored 83 percent on problems from the International Mathematics Olympiad, compared to 13 percent for GPT-4o, and reached the 89th percentile in Codeforces coding competitions. Developers have already used it to build agentic applications for customer support, supply chain optimization, and financial forecasting. Beyond pure reasoning, o1 now supports function calling, developer messages, Structured Outputs, and vision capabilities, making it practical for production workflows where complex decision-making meets real-world deployment.