Large language models have significantly advanced software development by automating tasks such as code generation and sophisticated bug detection. Bug detection presents a complex challenge that requires AI models to engage in deep logical reasoning beyond simple pattern matching. A comparison between two prominent OpenAI models, OpenAI o1 and OpenAI 4.1, was conducted to evaluate their performance in detecting subtle logic-heavy bugs. The results showed that OpenAI o1 slightly outperformed the newer model, with a slight advantage in complex scenarios. Language-specific breakdowns revealed interesting patterns, with OpenAI o1 performing well in Python and TypeScript, but excelling in Rust and Go. The analysis attributed the variance in results to architectural differences and the presence or absence of explicit reasoning steps in the models. The study highlights the value of explicit reasoning capabilities for detecting logic-heavy bugs, particularly in environments where logical deduction is crucial.