The article compares two AI models, OpenAI's o3-mini and DeepSeek's R1, for bug detection in software engineering. The evaluation was conducted on a curated benchmark of real-world-inspired programs with subtle but critical bugs in Python, TypeScript, Go, Rust, and Ruby. While o3-mini outperformed across the board, DeepSeek R1 showed competitive performance in Rust and TypeScript, hinting at solid reasoning capabilities. Analysis suggests that o3-mini's structured reasoning process makes it stronger in detecting concurrency issues and logical flaws, particularly in languages like Python, Go, and other common languages where pattern-rich bugs benefit from extensive memory. In contrast, DeepSeek's strengths lie in areas with thinner training data, such as Ruby and Rust, where generalization is key. The comparison highlights the importance of language-dependent model performance and the need for reasoning-based approaches to catch real bugs in codebases.