Comparing performance of LLMs is not very interesting

2026-03-24 · Source: Ehud Reiter's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, medium

Summary

Comparing the performance of different Large Language Models (LLMs) in research papers is often uninteresting and quickly obsolete due to the rapid evolution of these models. While NLP papers frequently include tables comparing LLM performance, these data points become irrelevant as newer versions (e.g., GPT 5.4 replacing GPT-4o) emerge. Instead, the focus should shift to identifying shared behaviors or failure modes across multiple LLMs, which can reveal generic limitations of current LLM technology. Examining the maximum performance across a set of LLMs can also indicate how close a problem is to being solved. A study on LLMs as medical assistants, for instance, found that while specific model comparisons were fleeting, the overall poor performance of LLMs compared to Google search, and their universal struggle with user communication, offered more valuable, lasting insights.

Key takeaway

For AI Scientists evaluating LLM capabilities, you should prioritize qualitative insights and shared behavioral patterns across models over transient quantitative performance benchmarks. Focus your experimental design on understanding fundamental limitations and user interaction challenges, as these findings will remain relevant longer than specific model-to-model comparisons. Invest in high-quality human-centric experiments to uncover durable truths about LLM effectiveness.

Key insights

Quantitative LLM performance comparisons quickly become obsolete; qualitative insights offer lasting value.

Principles

• Focus on shared LLM behaviors.
• Identify fundamental LLM limitations.
• Qualitative insights endure longer.

Method

Conduct high-quality experiments with human users to understand LLM effectiveness, rather than relying on artificial tasks or LLM simulations.

In practice

• Test multiple LLMs for common failure modes.
• Prioritize user interaction studies.
• Evaluate LLMs for communication challenges.

Topics

• LLM Evaluation
• NLP Research Methodology
• Qualitative Analysis
• Model Obsolescence
• Medical AI Applications

Best for: AI Scientist, AI Researcher, AI Student, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ehud Reiter's Blog.