Effective bug detection in software development relies heavily on AI-powered tools, with a particular focus on leveraging logical reasoning capabilities to uncover subtle yet serious bugs that traditional approaches may overlook. A comparison of two advanced AI language models, OpenAI o1-mini and Anthropic Sonnet 3.5, was conducted to evaluate their capabilities in identifying hard-to-detect software bugs. The results showed that Anthropic Sonnet 3.5 significantly outperformed OpenAI o1-mini across a range of programming languages, with substantial advantages observed in languages such as Ruby, TypeScript, and Go. This suggests that the built-in reasoning capabilities of Sonnet 3.5 provide meaningful advantages, particularly in scenarios where traditional pattern recognition alone falls short. The evaluation highlights the value of reasoning-enhanced models like Sonnet 3.5 in detecting complex software bugs and underscores their potential to significantly improve software reliability and developer productivity.