The evaluation of two OpenAI language models, o1-mini and o4-mini, aimed to determine which performs better at identifying hard-to-find bugs within complex software systems. The test involved introducing 210 realistic, challenging bugs across five programming languages, including Go, Python, TypeScript, Rust, and Ruby. The results showed that o4-mini slightly outperformed o1-mini in overall bug detection, with a notable advantage in detecting bugs in languages like Python where logic errors are common. The performance difference was attributed to the reasoning component of o4-mini, which enables it to logically deduce and simulate code execution, making it effective in detecting subtle, logic-driven errors. This suggests that traditional pattern-based models may excel in well-documented, structured environments, whereas reasoning-enhanced models like o4-mini are better suited for scenarios involving nuanced, logic-driven errors.