How to evaluate and benchmark Large Language Models (LLMs)
Nov 4, 2025
Summary
This post details the principles of good LLM benchmarking, including difficulty, diversity, usefulness, reproducibility, and data contamination. It also explores multiple-choice and classification benchmarks like MMLU, GSM8K, and HumanEval, providing practical insights into how to evaluate and benchmark LLMs.
More from the LLM Evaluation Framework trail
- What do LLMs think when you don't tell them what to think about?
- Together Evaluations now supports comparing top commercial APIs vs. open source models
- Fine-tuning open LLM judges to outperform GPT-5.2
- How to choose the right open model for production
- Dynamic AI agent testing for the real world with Collinear Simulations and Together Evals