As code complexity rises, developers face increasing difficulty detecting subtle bugs. Researchers have been exploring AI-driven code review tools to catch these elusive errors, and a recent evaluation compared two leading language models, Anthropic Sonnet 3.7 and OpenAI o1, to assess their effectiveness at detecting challenging software bugs. The results showed that Anthropic Sonnet 3.7 significantly outperformed OpenAI o1 across all tests, with notable advantages in languages like Go and TypeScript due to its enhanced reasoning capability. This architecture enables the model to better understand complex logic and concurrency issues, particularly in less extensively represented languages. The evaluation highlights the value of reasoning-enhanced models in AI-assisted debugging, demonstrating their potential to become essential tools for improving software verification accuracy and effectiveness.