A study evaluates the performance of Large Language Models (LLMs) on the Equivalence Class Problem, revealing that while reasoning models perform better than non-reasoning ones, both struggle with long-chain reasoning tasks. The findings highlight the challenges LLMs face in complex reasoning scenarios, indicating room for improvement in their capabilities.