Important LLM benchmarks to know about

This article clarifies common misconceptions about LLM performance by explaining important benchmarks used to evaluate language models. It covers reasoning benchmarks like ARC-AGI-2 and GPQA Diamond, coding benchmarks such as Terminal-Bench 2.1, ProgramBench, SWE-Marathon, SWE-Bench, and LiveCodeBench. The author emphasizes that no single model excels at everything and advises evaluating models based on intended use cases (e.g., coding, reasoning, image input). The article also recommends using Artificial Analysis for aggregated benchmark scores and warns against vendor hype. It is a technical guide relevant to IT/technology professionals selecting LLMs for specific tasks.