Journal of Information Technology Review Pit Pichappan 17 3 2026 https://doi.org/10.6025/jitr/2026/17/3/105-119 https://www.dline.info/jitr/fulltext/v17n3/jitrv17n3_1.pdf The rapid proliferation of Large Language Models (LLMs) has established benchmark evaluations as the primary mechanism for assessing model capability and technological progress. However, growing concerns regarding benchmark validity, data contamination, and the interpretability of aggregate scores highlight a critical gap in understanding how these metrics reflect the broader LLM ecosystem. This study addresses this gap by conducting a comprehensive descriptive analysis of benchmark performance and capability stratification across contemporary LLMs. Utilizing the Comprehensive LLM Benchmark Dataset, comprising 390 model-benchmark observations from 2022 to 2024, we employ descriptive statistics, density estimation, and performance-tier categorization to map the performance landscape. Our findings reveal a negatively skewed distribution with a high median but substantial variability, indicating that while baseline competencies are standardizing, significant capability gaps persist. Furthermore, the analysis identifies distinct capability strata, with Strong and Top Tier models accounting for over 56 per cent of observations, yet a substantial proportion of Weak and Moderate performers remain. These results demonstrate that the contemporary LLM landscape is highly stratified rather than homogeneous. This stratification highlights the need for delicate evaluation. Ultimately, this research underscores that aggregate benchmark scores often obscure underlying heterogeneity in capabilities. We conclude that future evaluation frameworks must evolve toward multidimensional, capability-oriented methodologies to accurately capture model maturity and real-world utility, providing a foundational baseline for subsequent research on scaling laws and architectural effectiveness.