OpenAI o1 Preview: Capability Expansion Shifts from Training Scaling to Inference-Time Compute
OpenAI releases o1-preview and o1-mini, models that invest more reasoning compute before answering.
Inference-time compute becomes a new capability lever; the industry shifts from instant generation to problem-solving with allocatable thinking budgets.
Reinforcement learning and long-chain reasoning enable performance on math, code, and science problems to scale with reasoning budget.
Model pricing, latency, and capability are no longer a fixed curve; the importance of inference infrastructure and task routing rises.
High-value complex tasks can tolerate higher latency and cost; enterprises should select models based on task value rather than uniform token price.
Observe verifiable reasoning, error recovery, long-chain cost, and controllability of reasoning process.