benchmarks

**Benchmarks** provide the standardized metrics needed to compare the performance of different AI models and hardware architectures. As the field moves at light speed, benchmarks serve as the vital scoreboard that tells us which systems are truly pushing the boundaries of what is possible.

We cover the entire spectrum of AI measurement, from foundational reasoning tests like MMLU to specialized benchmarks for coding (HumanEval), math (MATH), and multimodal understanding. We also discuss the growing importance of "Dynamic Benchmarks"—tests that evolve to prevent models from simply "memorizing" the training data. Our analysis extends to hardware benchmarks as well, measuring the "tokens-per-second" and "energy efficiency" of NPU and GPU clusters.

Ultimately, benchmarks are about more than just scores; they are about understanding the "Trajectory of Progress." By tracking how models improve over time, we help our readers anticipate when AI will reach human-level performance in specific professional domains.