DEV post: pair per question before comparing models

On November 5, 2026, DEV Community's AI channel published an article on how to compare two models more carefully. Its central claim is that when two agents posted a five-point lead on the same item set, the gap was frequently a property of the pairing rather than of the models themselves. Taking independent averages for the two arms discards the single most useful fact in the run: that both sides answered the exact same questions. That shared structure is described as the cheapest noise reducer available at small sample sizes. The author therefore recommends pairing per item, seeding both arms identically, and reporting an interval on the per-item difference rather than a single headline number. The entire setup is said to cost roughly thirty lines of code and one afternoon to put together, which places it well within reach of a small eval harness or an internal benchmarking script. No external data is required, and the method does not depend on any particular model, framework, or language. The article frames the piece as practical guidance for anyone running A/B comparisons of LLM agents on a shared prompt set, where sample sizes are often modest and per-question variance can easily drown out real differences between systems. By keeping responses anchored to the same items and reporting uncertainty on the per-item deltas, practitioners can separate genuine signal about model capability from artifacts introduced by how the comparison was constructed.