O1 Model: Definition and Examples

OpenAI's O1 model represents a major breakthrough in the field of generative artificial intelligence. Unlike previous models such as GPT-4, O1 uses an internal "chain-of-thought" technique: before responding, it breaks down the problem into logical steps, explores different approaches, and verifies its reasoning. This process, invisible to the user, allows it to achieve remarkable performance on tasks requiring structured thinking.

The model comes in several variants: O1 (full version), O1-mini (lightweight and faster), and O1-pro (premium version with more reasoning time). Each variant offers a different trade-off between depth of reflection, response speed, and cost of use. O1-mini is particularly suitable for programming tasks, while O1 excels in scientific and mathematical domains.

O1's architecture is based on reinforcement learning applied to reasoning. The model was trained to "think" before answering, which fundamentally distinguishes it from classic autoregressive models that generate responses token by token without a planning phase. This approach allows it to solve olympiad-level math problems, competitive programming tasks, and scientific reasoning with a significantly higher success rate.

In prompt engineering, working with O1 requires a different approach. The model handles problem decomposition itself, making classical techniques like explicit chain-of-thought or few-shot prompting less necessary or even counterproductive. It is better to formulate clear and direct instructions, letting the model structure its own reasoning.