As software complexity grows, the ability to reliably identify subtle, intricate bugs becomes increasingly important. The emergence of AI-powered tools has provided valuable aids in software bug detection, with two notable language models—OpenAI 4o and Anthropic Sonnet 3.7—standing out as strong contenders. A comparison between these models was conducted using a dataset of 210 deliberately introduced subtle bugs across several programming languages, highlighting their strengths and weaknesses. The results show that Anthropic Sonnet 3.7 detected more bugs than OpenAI 4o, with notable superiority in languages like Ruby and TypeScript, where its reasoning-based approach excelled. This suggests that models like Anthropic Sonnet 3.7 may hold greater potential for addressing complex or less frequently encountered programming languages. The evaluation also demonstrated the complementary strengths of pattern-based and reasoning-based AI models, emphasizing the importance of combining these approaches to yield even more robust bug detection capabilities.