This article explores the emerging paradigm of reasoning models, which differ from traditional large language models (LLMs) in their problem-solving approach. Unlike conventional LLMs that generate responses in a fixed manner, reasoning models allocate variable computational effort to thinking before answering, improving their ability to decompose problems, detect errors, and explore alternative..