Claude Sonnet 4.0, a reasoning-optimized large language model, was tested against its predecessor Sonnet 3.7 for bug detection in a dataset of over 200 self-contained programs created across five programming languages. The results showed that both models caught roughly 14% of injected bugs, with minor variations across languages, indicating that improvements may lie more in reasoning style than raw accuracy at this stage. Despite not outperforming Sonnet 3.7, Claude Sonnet 4.0 demonstrated a solid consistency and substantial overlap in bugs caught, suggesting a robust underlying AI framework. The evaluation highlights distinct internal heuristics or reasoning strategies between the two models, offering opportunities for optimization and improvement in future iterations of reasoning-first models.