Anthropic's Sonnet 3.7 AI model outperformed OpenAI's o1-mini in detecting complex software bugs across five programming languages, particularly excelling in TypeScript, Rust, and Ruby. The model's built-in reasoning capability enabled it to catch logical inconsistencies and nuanced semantic issues more effectively, highlighting its superiority in detecting subtle bugs that evade common detection methods. While o1-mini performed better in mainstream languages like Python, the results suggest that reasoning-based approaches provide substantial advantages when dealing with less common languages or complex code logic. The evaluation demonstrates the significant potential of AI models incorporating explicit reasoning for advanced software bug detection tasks.