The author tested two OpenAI language models, o4-mini and o1, on their ability to detect challenging bugs in code. The tests used a diverse dataset of 210 realistic yet difficult-to-detect bugs across five programming languages: Python, TypeScript, Go, Rust, and Ruby. While both models performed equally well overall, they showed distinct strengths in different languages, with OpenAI o4-mini excelling in Python due to its better understanding of dynamic constructs and concurrency challenges, while OpenAI o1 performed better with TypeScript due to its handling of static typing intricacies. The results highlight the need for continued improvements in AI reasoning and model training to boost their utility in real-world debugging scenarios, suggesting that these models will become indispensable partners for developers.