The article compares two advanced language models, OpenAI 4o-mini and DeepSeek R1, to assess their effectiveness in identifying hard-to-spot bugs across several programming languages. The authors generated a dataset of 210 programs with realistic bugs and tested the models on Python, TypeScript, Go, Rust, and Ruby. The results show that both models have comparable overall performance but exhibit varying strengths depending on the programming language involved. OpenAI 4o-mini excels in Python and Ruby due to its pattern recognition capabilities, while DeepSeek R1 performs better in TypeScript and Rust due to its logical reasoning abilities. A detailed breakdown of the results highlights the differences between the two models and suggests that integrating rapid pattern recognition and sophisticated logical reasoning into AI-driven software verification tools can significantly improve their reliability and efficiency.