The article compares two models from OpenAI, o1 and o3-mini, to assess their effectiveness in detecting complex bugs in real-world codebases. A benchmark of 210 buggy programs was created, with each program containing a subtle bug that could be challenging for developers to identify. The results show that o3-mini outperformed o1 across all five languages tested, catching 37 bugs compared to o1's 15. The key difference between the two models is their ability to perform structured reasoning, which enables o3-mini to trace logic and surface bugs that go beyond syntax or pattern matching. This allows o3-mini to excel in detecting bugs in languages like Rust and Ruby, where logical deduction is required. The article concludes that o3-mini is a clear winner for detecting real-world software bugs, making it an attractive choice for developers looking for a more effective tool in their code reviews.