# Claude Opus 5.5 Takes Top Spot as Artificial Analysis Launches Intelligence Index v5 > Source: > Published: 2026-10-28 06:50:27+00:00 *Artificial Analysis rebuilt its widely-cited AI benchmark this week, putting 60% of the score behind private test sets that labs can't study in advance. The reshuffle put Claude Opus 5.5 in first place and left open-weight models further behind than before.* Artificial Analysis launched Intelligence Index v5 in late October, and the leaderboard moved. Claude Opus 5.5, running at max reasoning effort, now sits at the top with a score of 58, ahead of OpenAI's GPT-6 Astra at 57 and GPT-6.1 Sol at 55. The three labs are separated by two points combined, which tells you how tight the frontier has become even as the test itself got harder. The bigger story is what changed underneath the scores, not just who's on top. For the first time, the majority of the index, 60% of it, runs on private datasets the labs have never seen, according to Artificial Analysis's own announcement. Two new evaluations anchor that shift, alongside three upgraded benchmarks, marking the biggest overhaul to the index since it launched. The general-knowledge side got an overhaul too, with updated versions of existing benchmarks replacing older, more saturated tests. This is the part that stings for the open-source side of the industry. The gap between the top closed model and the best open-weight model widened from 3 points to 12 points under the new methodology, according to Artificial Analysis. Kimi K3.1 Pro, from Moonshot AI, now leads the open-weight field at a score of 46, which puts it well back of Opus 5.5's 58. That's a reversal from the trend just a few months earlier. On the old v4.3 scoring, Moonshot's Kimi K3 had closed to within 6 points of the leader, and Artificial Analysis had itself noted that open-weight models had cut the gap roughly in half over the preceding year. Private test sets appear to hit open models harder, likely because open-weight labs have less room to use private evaluation partnerships or held-out data the way frontier labs with deeper benchmarking relationships can. Whether that's a fair read of actual capability or an artifact of how the new tests were built is exactly the kind of question r/LocalLLaMA has been arguing over since the release landed. [Moonshot AI releases Kimi K3.1 Pro open weight model to rival Claude](https://startupfortune.com/moonshot-ai-releases-kimi-k31-pro-open-weight-model-to-rival-claude/) Moonshot AI built K3.1 Pro as a mixture-of-experts model with 2.8 trillion total parameters and 104 billion active at once, handling a 1 million token context window. The faster Kimi K3.1 Pro Highspeed variant is already available through Moonshot's apps and API. - [Kimi K3.1 Pro open weight model release](https://startupfortune.com/moonshot-ai-releases-kimi-k31-pro-open-weight-model-to-rival-claude/) - [how Kimi K3.1 Pro compares to Claude Opus](https://startupfortune.com/moonshot-ai-releases-kimi-k31-pro-open-weight-model-to-rival-claude/) Meta's Muse Wonder Full, which scored 56 on the prior version of the index just two weeks before v5 shipped, is a useful marker of how fast this leaderboard moves and how little a single score tells you in isolation. A model that was effectively tied with GPT-6 Astra in mid-October now sits in a different competitive picture entirely, not because it changed, but because the ruler did. ## Why enterprises actually care about this number Artificial Analysis isn't a lab, and it doesn't sell models. That's exactly why procurement teams and infrastructure buyers cite it when justifying which model they're paying to run at scale. A two-point gap between GPT-6 Astra and GPT-6.1 Sol doesn't settle a vendor decision on its own, but when a company is choosing which model to build an agent stack on, or defending a seven-figure inference contract to a board, an independent composite score carries weight that a lab's own marketing never will. That's also the uncomfortable part of this reshuffle for buyers who locked in infrastructure decisions around the old index. If your vendor selection leaned on a benchmark that's just been rebuilt with a dozen-point open-weight penalty baked in, the math behind that decision changed overnight, even though the model you're running didn't. Alongside v5, Artificial Analysis also launched a companion tool called the Progress Index v1, which places every major language model released since November 2022 on a single capability scale. The idea is to let buyers see not just who's ahead today, but how fast the ground has shifted under them, something that matters more with each new version of the Intelligence Index landing just months after the last one. **Also read:** [Liquid AI releases LFM3 models small enough to run on a phone](https://startupfortune.com/liquid-ai-releases-lfm3-models-small-enough-to-run-on-a-phone/) • [SpAItial launches Echo-3 to turn iPhone scans into 3D digital twins](https://startupfortune.com/spaitial-launches-echo-3-to-turn-iphone-scans-into-3d-digital-twins/) • [ASMPT sales jump 49% on AI chip packaging as core equipment unit misses](https://startupfortune.com/asmpt-sales-jump-49-on-ai-chip-packaging-as-core-equipment-unit-misses/) *This article is posted in [AI News](https://startupfortune.com/category/ai/), check it out for more related stories.* ## Join the discussion [Open in the community →](https://startupfortune.com/community/) Almost there. 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