LLM Leaderboard Guide: Understanding MMLU, HumanEval, and MT-Bench

LLM benchmarks are widely cited but poorly understood. A model that scores 90 on MMLU and 85 on HumanEval might be terrible for your actual use case. This guide demystifies the major benchmarks, explains what they actually measure, and helps you pick models based on what matters for your workflow.

Best reference: The Open LLM Leaderboard on Hugging Face tracks standardized benchmark scores across hundreds of models. For hardware-matched recommendations (best model for your specific GPU), LLMHardware.io's model picker filters by benchmark performance and VRAM requirements.

MMLU: Knowledge Breadth

Massive Multitask Language Understanding tests knowledge across 57 academic subjects from elementary to professional level. A high MMLU score means the model has broad factual knowledge. It does NOT mean the model reasons well or follows instructions well. MMLU is highly correlated with training data quality and size — bigger, better-trained models score higher almost automatically.

HumanEval: Coding Ability

HumanEval measures the percentage of Python programming problems the model solves correctly with a single generation pass (pass@1). It's a reasonable proxy for coding ability on simple to medium-difficulty tasks. Limitations: Python only, relatively simple tasks, doesn't capture real-world debugging ability. Models fine-tuned on code (DeepSeek-Coder, CodeLlama, Codestral) score much higher than general models of similar size.

MT-Bench: Instruction Following

MT-Bench uses GPT-4 as a judge to score multi-turn conversations across writing, roleplay, extraction, math, coding, and reasoning. It's the best proxy for "how good is this model in a chat interface." A model scoring 7.5+ on MT-Bench generally feels capable and coherent in practice. The limitation: GPT-4's biases as a judge affect scores, and the benchmark has been partially contaminated by training data.