Ensuring code robustness and catching elusive bugs before deployment is becoming increasingly challenging as software complexity grows. Recently, I evaluated two large language models—OpenAI o1 and Anthropic Sonnet 3.5—to gauge their effectiveness at uncovering challenging bugs. Detecting these issues requires more than syntax checking; it demands deep logic comprehension, reasoning about concurrency, and nuanced understanding of language-specific complexities. The model that excelled in this task was Anthropic Sonnet 3.5, which demonstrated clear superiority across all tested scenarios, successfully identifying 26 out of 210 bugs, while OpenAI o1 identified only 15 out of 210 bugs. This significant difference underscores Sonnet 3.5's advantage, likely due to its embedded reasoning capability. The model notably excelled in detecting subtle logical issues, particularly in languages with fewer training examples, such as Go and Ruby, where it showed a significant edge over OpenAI o1.