The study compares two leading Large Language Models (LLMs), OpenAI's o4-mini and Anthropic's Sonnet 3.7, to evaluate their effectiveness in detecting intricate software bugs across various programming languages, including Python, TypeScript, Go, Rust, and Ruby. The evaluation dataset consists of 210 programs with realistic yet difficult-to-catch bugs introduced by the author. The results show that Anthropic Sonnet 3.7 outperforms OpenAI o4-mini in overall bug detection and performance by programming language, particularly excelling in languages like Go, Rust, and Ruby where logical reasoning capabilities are valuable. The study highlights the importance of balancing pattern recognition training with robust logical analysis processes to enhance software quality and developer productivity.