The article compares two AI models from OpenAI, o3-mini and 4.1, to assess their effectiveness in detecting hard-to-catch software bugs. The results demonstrate a clear overall advantage for o3-mini, which detected 37 out of 210 bugs compared to 16 detected by 4.1. A deeper examination of each language revealed detailed insights, with o3-mini performing strongly in languages like Python and Ruby due to its integrated reasoning capabilities. This highlights the potential benefits of incorporating reasoning steps into AI-driven bug detection processes, enabling more precise identification of nuanced logical errors. The results suggest that future AI models should balance sophisticated reasoning with extensive pattern recognition to revolutionize bug detection and transform software quality assurance processes.