The race to build bigger and better AI coding assistants is on, but a critical question lingers: do these models really *understand* code, or are they just good at mimicking it? A new research paper introduces "CodeMMLU," a benchmark designed to probe the actual comprehension abilities of Code Large Language Models (CodeLLMs). Unlike benchmarks focused on code generation, CodeMMLU poses nearly 20,000 multiple-choice questions spanning diverse software engineering topics. These questions test not just the model's ability to produce code, but also its deeper understanding of concepts like code analysis, defect detection, and software principles across multiple programming languages. The results are revealing. Even state-of-the-art CodeLLMs struggle with CodeMMLU. While these models excel at generating code, their comprehension often falls short when faced with complex scenarios. Interestingly, the research found no strict correlation between model size and performance, suggesting that data quality and training methods play a more significant role than sheer scale. Moreover, complex prompting techniques like "Chain-of-Thought" actually *hindered* performance on many tasks, indicating that forcing step-by-step reasoning isn’t always the best approach for knowledge-based questions. The findings from CodeMMLU have important implications for the future of AI-assisted software development. The benchmark offers a more reliable and accurate way to assess CodeLLM comprehension, going beyond superficial metrics. This will help researchers fine-tune models and develop new training methodologies to enhance comprehension. The ultimate goal? Creating code assistants that not only generate code but truly understand the intricacies of software development, thus becoming more reliable and capable partners for human developers.