AI models are increasingly used to generate code, but how well do they review it? A comparison of OpenAI's o1 and o1-mini models was conducted on their ability to detect real-world software bugs across five programming languages. The dataset consisted of 210 programs with realistic, difficult-to-catch bugs introduced in each one. While both models struggled across the board, o1 consistently outperformed o1-mini, especially in TypeScript and Rust. Analysis suggests that o1 has broader pattern exposure from training data, enabling better detection even in non-reasoning tasks, whereas o1-mini prioritizes speed and simplicity over depth. The results highlight the importance of deeper logic tracing, especially for race conditions and shared state management. Ultimately, o1 is recommended for better bug detection accuracy, particularly in TypeScript or Rust, while o1-mini can be used for lighter tasks where compute efficiency matters more than precision.