Comparative LLM Analysis: Benchmarking Language Model Performance

Large language model evaluation has become dominated by single-number leaderboards that rank models using aggregate scores across diverse tasks. While these leaderboards provide useful high-level comparisons, they obscure critical details about model behavior, capabilities, and limitations that matter for responsible deployment. This paper critiques current LLM benchmarking practices and proposes a framework for comparative analysis built on three principles: parity of information (standardized evaluation conditions), uncertainty and risk reporting (confidence intervals and safety metrics), and benchmark cards (comprehensive metadata about datasets, tasks, and protocols). Through case studies in multidomain evaluation, I demonstrate how transparent, multidimensional comparisons can support responsible model development and deployment while enabling fairer assessment of competing approaches. The framework provides both theoretical foundations and practical tools for moving beyond reductive metrics toward evaluation practices that capture the dimensions of model performance most relevant for beneficial real-world deployment.