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HLE-Diamond leaderboard: reasoning outpaces knowledge, effort costs add up

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Artificial Analysis@ArtificialAnlys

We've launched our HLE-Diamond leaderboard, our independent evaluation of the refined 1,000-question version of Humanity's Last Exam, as part of v5 of the Artificial Analysis Intelligence Index HLE-Diamond was released by @CAIS and @scale_AI after a year of cleaning and refining the original Humanity's Last Exam dataset with input from the research community. It has 1,000 closed-ended questions, split evenly between reasoning and knowledge, and 272 of them include an image. We run every question once without tools, using the official HLE system prompt. GPT-5.6 Luna (medium) grades each answer against the reference answer with the official HLE judge prompt, and we report the share of questions judged correct (pass@1). Key takeaways: ➤ Only 17 model variants (~13%) score above 50% today: Gemini 4 Carbon (high) leads at 69%, followed by Claude Opus 5.5 (max) at 68% ➤ Models score higher on reasoning than on knowledge: Over 90% of the models we measured do better on the reasoning portion of the benchmark. Gemini 4 Carbon (high) leads both, at 80% on reasoning and 59% on knowledge. Claude Opus 5.5 (max) and GPT-6 Astra (max) both score 78% on reasoning, and Opus 5.5 is ahead on…

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Why it matters

Shows what extra effort buys: Opus 5.5 gains 26 points from low to max effort at about 35x the cost per task. It also shows models are much stronger on reasoning than knowledge questions.

Terms in this piece · Glossary
  • open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
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