The author of the text conducted a head-to-head evaluation of two OpenAI models, `o3` and `o1`, to investigate whether reasoning-enhanced LLMs can outperform standard models in detecting difficult-to-catch software bugs. The evaluation dataset consisted of 210 intentionally bugged programs spanning multiple languages and domains. The results showed that `o3` successfully detected 38 bugs, while `o1` caught only 15, highlighting the advantage provided by the additional reasoning step incorporated in `o3`. The performance breakdown by language revealed significant advantages for `o3` in detecting bugs in languages such as Go, Rust, and Ruby, where the model's reasoning capabilities excelled. The author attributes this to the explicit reasoning phase allowing the model to logically navigate unfamiliar or complex error scenarios.