AI Benchmarking: How to Measure Progress Between Models

Why Fair Model Comparison Is Harder Than It Looks?

Comparing two AI models fairly is less straightforward than it appears, and a surprising fraction of published comparisons don't hold up to close scrutiny once you look at the methodology behind the numbers. Measuring genuine progress between models requires controlling for several variables that are easy to overlook when a single headline score is all that gets reported.

Three Variables That Distort a Comparison
Prompt and evaluation methodology differences that reflect how a model was evaluated, not what it can do.
Training data contamination benchmark questions leaking into subsequent training runs.
Mismatch between what's measured and what the comparison claims to show.

The third and most consequential variable is what's actually being measured relative to what the comparison claims to show. A benchmark focused on narrow factual recall says little about reasoning ability, and a benchmark focused on short single-turn tasks says little about performance on long, multi-step agentic work.AI benchmarking done rigorously means selecting (or designing) a benchmark whose task structure actually resembles the deployment scenario the comparison is meant to inform, rather than defaulting to whichever benchmark happens to be most widely cited.

The Most Reliable Approach

For technical teams making a build-or-buy or model-selection decision, the most reliable approach is running models against an internal evaluation set built from the team's own real task distribution, in addition to (not instead of) public benchmarks because public benchmark scores, however carefully controlled, are still a proxy for the specific capability a given deployment actually needs.
TL;DR
✓ Comparing two AI models fairly is less straightforward than it appears, and a surprising fraction of published comparisons don't hold up to close scrutiny...
✓ The first variable is prompt and evaluation methodology.
✓ The second variable is training data contamination whether benchmark questions or close variants of them appeared in a model's training data.