Large language models are getting better at generating code, but their ability to detect subtle bugs is still a topic of debate. A comparison of two small OpenAI models, o3-mini and 4o, was conducted to see how well they perform in detecting bugs in real-world software. The authors built a benchmark of 210 small programs with realistic logic errors and edge cases, and introduced tiny bugs into each program. The results show that o3-mini outperforms 4o across the board, especially in languages with less training representation. This is likely due to o3-mini's structured reasoning approach, which gives it an advantage in situations where logic, structure, or intent need to be inferred. In contrast, 4o seems more tuned for broad task coverage and performance speed, but struggles slightly in areas that require deep structural understanding. The study highlights the importance of using models like o3-mini for AI code review and bug detection, especially for logic-heavy or backend-heavy stacks.