LLM Benchmarks Are Junk Science
Last Updated on April 2, 2026 by Editorial Team
Author(s): Kaushik Rajan
Originally published on Towards AI.
An Oxford review of 445 benchmarks found 84% lack basic statistical testing. Models score 90% on standard tests but 2% on unseen problems. A 5-question smell test for any benchmark claim.
Over the past year, I’ve evaluated more than sixty artificial intelligence (AI) tools for production use. Every vendor came armed with benchmark numbers: leaderboard rankings, accuracy percentages, scores on tests with impressive names. The numbers looked scientific. Decimal points. Bold-faced winners. Tables sorted by fractions of a percent.
This article critiques the reliability of benchmarks used in assessing artificial intelligence tools, revealing that a substantial majority fail to employ statistical testing, leading to misleading claims about model performance. It discusses the disconnect between benchmark results and real-world performance, highlighting issues such as contamination of test data and the use of vague definitions in benchmarks. The author emphasizes the need for more rigorous evaluation standards and proposes a checklist to assess benchmark validity.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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