The article compares two Large Language Models (LLMs), OpenAI 4o-mini and Anthropic Sonnet 3.7, in their ability to detect subtle software bugs across multiple programming languages, including Python, TypeScript, Go, Rust, and Ruby. A custom evaluation dataset of 210 programs with realistic yet difficult-to-catch bugs was created. The results show that Anthropic Sonnet 3.7 outperforms OpenAI 4o-mini in detecting bugs, especially in languages like TypeScript, Go, Rust, and Ruby, where logical reasoning capabilities are beneficial. This can be attributed to Sonnet's built-in planning or "thinking" step, which allows it to engage in explicit logical reasoning before generating responses. In contrast, OpenAI 4o-mini performs well in pattern-rich languages but falls short when dealing with languages requiring deeper logical evaluation. The study highlights the potential benefits of combining robust pattern recognition with logical reasoning capabilities to create more versatile bug detection tools.