Artificial Analysis rebuilds AI ranking; Claude Opus 5.5 leads at 58

Artificial Analysis, an independent firm that benchmarks AI models, released version 5 of its Intelligence Index on October 27, 2026. The most important change: about 60% of the composite score now comes from private test sets or held-out answers, and under the new rules Claude Opus 5.5 leads at 58 while the best open-weight model trails by 12 points, up from 3.

The Index is one of the most closely watched rankings of AI models, and Artificial Analysis neither builds nor sells models, which is why buyers cite it when choosing what to run. Version 5 changes the ruler itself, so many scores move even where the models did not.

What Intelligence Index v5 changes

LavX News describes v5 as the largest update since January, and Trending Topics calls it the biggest update since the start of the year. Startup Fortune goes further and calls it the biggest overhaul since the index launched; the sources differ on that framing.

The central change is the move to private data. About 60% of the Index now relies on private test sets or held-out answers, meant to stop models from simply being trained on known test questions. Artificial Analysis’s own summary of the release says that for the first time, the majority of the Index is built on private datasets, with two new benchmarks and three upgrades.

The Index evaluates models across four categories weighted at 25% each: knowledge work, coding, science and general capabilities. The nine evaluations in v5.0 are AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset) and Terminal-Bench-Science 0.1. Artificial Analysis says it runs all evaluations independently.

New and upgraded benchmarks

• FrontierCode 2.0, from AI company Cognition, tests agents on 132 real-world coding tasks in open-source repositories, graded on whether a maintainer would merge the change.
• Terminal-Bench-Science evaluates 70 agentic scientific research tasks run in a terminal environment. “Agentic” here means the model works through multi-step tasks on its own, using tools, rather than answering a single question.
• HLE-Diamond replaces Humanity’s Last Exam with a refined 1,000-question set split between reasoning and knowledge.
• AA-Omniscience v1.1 removes saturated and public questions and adds new ones. Trending Topics describes the general-knowledge category as including a test that penalises hallucinations.
• CritPt v2 now runs on a private 40-question hard subset with code-execution tooling.

Among the tests dropped are SciCode and the knowledge-work test GDPval-AA. Both carried weight in the previous design: in v4.1, GDPval-AA v2 was the highest-weighted evaluation at 20%, and SciCode counted for 8%.

Item	Intelligence Index v5
Release date	October 27, 2026
Evaluations	Nine
Category weighting	Four categories, 25% each
Private or held-out share	About 60%
Agentic share of weight	55%, but roughly 90% of output tokens
Median output tokens per task	59,000, up from 35,000

The new ranking: Opus 5.5 holds first, Astra climbs

Claude Opus 5.5 at max effort leads the Index at 58, followed by GPT-6 Astra (max) at 57 and GPT-6.1 Sol (max) at 55. Anthropic and OpenAI occupy the top five positions. Artificial Analysis’s own leaderboard also lists Opus 5.5 at its “xhigh” setting at 57, tied with GPT-6 Astra.

Model (maker)	Previous version	v5
Claude Opus 5.5 (Anthropic)	Top spot	58
GPT-6 Astra (OpenAI)	53	57
GPT-6.1 Sol (OpenAI)	n/a	55
Claude Sonnet 5.5 (Anthropic)	56	54
Gemini 4 Argon (Google)	53	50
Muse Wonder Full (Meta)	56	50

Scores and previous-version comparisons as reported by Trending Topics.

No model wins all nine individual tests; Opus 5.5 leads three of them. Meta’s Muse Wonder Full is the clearest example of the ruler moving: it scored 56 on the prior version two weeks before v5 shipped and was effectively tied with GPT-6 Astra in mid-October, Startup Fortune notes, and it now sits elsewhere “not because it changed, but because the ruler did”.

Why open-weight models fell further behind

Open-weight models, whose weights are published so anyone can run them, are hit hardest. The gap between the best proprietary model and the best open-weight model widened from 3 points in v4.3 to 12 points.

• Kimi K3.1 Pro (Moonshot AI) falls from 55 to 46 and remains the best open model, although Moonshot has not yet released its weights. Unicorn API covered its launch in Moonshot launches Kimi K3.1 Pro with hacking skills deliberately curbed.
• DeepSeek V4.1 Flash drops from 45 to 32.
• MiMo-V2.6-Pro (Xiaomi) loses 16 points, ending at 30.
• Muse Spark Open (Meta), the best open model from the US, sinks from 39 to 25.
• Mistral Large 4 Preview, not yet open, scores 25.

Among models whose weights are actually released, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) score 32, behind Anthropic’s small Claude Haiku 5.5 (max) at 36. The best model with released weights now sits 26 points behind the leader.

The largest gaps appear on the new, harder tests. On the revised Humanity’s Last Exam, Kimi scores 37% and Opus 68%; on the scientific agent tasks, Kimi reaches 34% and GPT-6 Astra 63%, while most open models stay below 10%.

That reverses a recent trend. Startup Fortune notes Artificial Analysis had itself observed that open-weight models cut the gap roughly in half over the preceding year. Startup Fortune’s own reading is that private test sets “appear to hit open models harder”, likely because open-weight labs have less room for private evaluation partnerships than frontier labs; that is the outlet’s interpretation, not a finding from Artificial Analysis.

Costs and effort settings matter more in v5

The new tests ask much more of models. Median output tokens per task rose by two-thirds, from 35,000 to 59,000, driven by agentic evaluations that make up 55% of the Index weight but roughly 90% of output tokens.

Cost per Index task varies more than 13-fold among leading models: GPT-6.1 Terra (max) costs $0.96 per task and Claude Fable 5.1 (max) $13.04. A run of Claude Opus 5.5 at its highest setting costs about $10 per task, according to Artificial Analysis, against about $1.27 for GPT-6.1 Sol. GPT-6.1 Sol reaches its 55 at roughly one-eighth of the leader’s cost. For more on the cheaper OpenAI model, see OpenAI releases GPT-6.1 Terra at half the API price of GPT-5.6 Terra.

Effort settings, which control how much computation a model spends reasoning before it answers, now produce wider spreads. Claude Opus 5.5 ranges from 35 at low effort to 58 at max, a 10x cost increase. Artificial Analysis calculates cost per task from input, cache hit, cache write, reasoning and answer token prices, divided by task count and weighted by each evaluation’s Index weight. It introduced cost, time and tokens per task as standard metrics in v4.1 in June.

What it means for builders

For teams picking a model for agents or production workloads, three points stand out.

1. Score depends on setting. A single model’s number can move by more than 20 points between low and max effort. When comparing vendors, compare the effort level you would actually pay for.
2. Cost per task can outweigh small score differences. A 3-point gap between Opus 5.5 and GPT-6.1 Sol comes with roughly an eightfold cost difference. Startup Fortune points out that a two-point gap does not settle a vendor decision on its own.
3. Old comparisons may no longer hold. Teams that chose infrastructure based on the earlier index face changed numbers even though their model did not change, Startup Fortune notes.

The open-weight picture is less clear-cut than the headline gap suggests. Kimi K3.1 Pro leads the entire field on the agent test Terminal-Bench 4.0, ahead of every proprietary model. Builders whose workloads resemble a specific benchmark should look at per-evaluation results rather than the composite alone.

Limits and open questions

Rankings depend heavily on which tests a benchmarking firm chooses and how it weights them: in the previous version, Kimi K3.1 Pro was fourth, three points behind the leader. Because most of the test data is now secret, the results can no longer be independently verified from outside; Artificial Analysis keeps the data private precisely so models cannot be trained on known questions. Whether the open-weight drop reflects real capability or how the new tests were built is the question r/LocalLLaMA has been debating since release.

A companion Progress Index

Alongside v5, Artificial Analysis launched the Progress Index v1, which places models released since November 2022 on a single capability scale across multiple benchmarks. It is meant to show how underlying capability changes over time, while the Intelligence Index remains the primary measure for current use cases. Startup Fortune frames it as a way for buyers to see how fast the ground has shifted, not just who leads today.

What to watch next

• Open weights for Kimi K3.1 Pro. The best open model in v5 has not yet had its weights released. Once they ship, the gap between the leader and models anyone can download may narrow from the current 26 points.
• The race at the top. Anthropic and OpenAI are within 3 points of each other at the top, with Opus 5.5 at 58, GPT-6 Astra at 57 and GPT-6.1 Sol at 55, and they hold all five top places. Google’s Gemini 4 Argon and Meta’s Muse Wonder Full sit 8 points behind the leader at 50. New releases from any of these makers will be scored against a harder test.
• Pressure on transparency. With most data private and outside verification no longer possible, expect continued debate over whether private tests measure capability fairly across open and closed models.
• Cost trends. With output tokens per task up by two-thirds, how makers price high-effort reasoning will shape which models look attractive on the cost-versus-intelligence chart.