In Production: Reading Model Evals Critically

The domain’s production capstone: how to read a vendor eval table, why cross-vendor comparisons mislead, and why your own numbers always win.

Content last verified 2026-09.

Lessons

1. The Vendor Eval Table
2. Apples to Apples Is Hard
3. Your Numbers Win

Sources

• Liang et al. (2022) — Holistic Evaluation of Language Models (HELM): standardized conditions for model comparison
• Chiang et al. (2024) — Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
• Chen et al. (2021) — Evaluating Large Language Models Trained on Code (HumanEval, pass@k)
• Zheng et al. (2023) — Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• Hendrycks et al. (2020) — Measuring Massive Multitask Language Understanding (MMLU)