The comparison of OpenAI 4.1 and OpenAI o1-mini models highlights their strengths and limitations in identifying intricate software bugs across various programming languages, with OpenAI 4.1 demonstrating a noticeable edge over o1-mini, especially in logic-heavy contexts like Rust and Go. The results illustrate the potential for these models to uncover nuanced errors typically overlooked by conventional methods, while also emphasizing the importance of ample data exposure in traditional pattern-based AI modeling. Notably, OpenAI 4.1's logical reasoning capabilities provide a clear advantage in languages with fewer training examples, such as Rust and Go, whereas o1-mini maintains its effectiveness in commonly-used languages like Python. The analysis suggests that integrating advanced logical reasoning capabilities into models like o1-mini could potentially improve its overall performance across diverse language contexts.