The comparison of two advanced AI models, Anthropic's Sonnet 3.5 and OpenAI's 4o-mini, reveals that Sonnet 3.5 outperforms 4o-mini in detecting challenging bugs across multiple programming languages, including Go, Python, TypeScript, Rust, and Ruby. The results underscore the difficulty of the task but also highlight the promising potential AI holds for enhancing software verification practices. Sonnet 3.5's superiority can be attributed to its architectural emphasis on a reasoning phase before generating outputs, allowing it to interpret and logically deduce code behavior more effectively. In contrast, 4o-mini's stronger performance in languages like Python and Rust highlights its reliance on rapid, pattern-based recognition. The comparison suggests that integrating explicit reasoning processes into AI-driven bug detection can significantly enhance model performance, especially in contexts where mere pattern recognition is insufficient.