o1 is built around a single insight: some problems need deliberate, multi-step reasoning rather than pattern matching at scale. Before generating an answer, the model works through logic chains internally—visible to you—on math proofs, bug-heavy code, and scientific questions where a wrong answer is worse than a slow one. It costs roughly 2–3x more per token than GPT-4o and takes longer to respond, making it a specialist tool rather than a daily driver. The real catch is knowing when you actually need it; using o1 for a summarization task or casual question is like hiring a surgeon to tie your shoes.