---
id: "lib-2026-10-27-artificial-analysis-intelligence-index-v5"
type: "library"
title: "Artificial Analysis Intelligence Index v5"
lang: "EN"
org: "Artificial Analysis"
source_type: "Report"
date: "2026-10-27"
week: "2026-W44"
url: "https://nosov.ai/library/lib-2026-10-27-artificial-analysis-intelligence-index-v5/"
original_url: "https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v5"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "Independent benchmarking firm Artificial Analysis publishes a reweighted composite index with per-benchmark scores, costs and token use; checkable and widely cited, though the weighting choices are its own and the index runs in its own harness."
summary_basis: "full-text"
original_minutes: "8"
tags: ["ai technology", "benchmarks", "open weight gap", "eval methodology"]
frames: ["verification-capacity"]
---

# Artificial Analysis Intelligence Index v5

Tier A (Strong evidence): Independent benchmarking firm Artificial Analysis publishes a reweighted composite index with per-benchmark scores, costs and token use; checkable and widely cited, though the weighting choices are its own and the index runs in its own harness.

Summary based on the full text.

## In short

Artificial Analysis (27 October 2026) announces version 5 of its Intelligence Index, a composite of nine evaluations weighted equally across knowledge work, coding, science and general knowledge. Most of the index now rests on private or held-out tests, which reduces the scope for models trained on the answers. Anthropic and OpenAI hold the top five places, open-weight models fall further behind, and effort settings now swing scores and cost far more than before.

## Key numbers

- 60%: share of the v5 index built on private test sets or held-out answers
- 58: top score, Claude Opus 5.5 (max), ahead of GPT-6 Astra (57) and GPT-6.1 Sol (55)
- 3 to 12 points: gap between the best open-weight model and the leader, v4.3 to v5
- 35 vs 58: Claude Opus 5.5 score at low versus max effort, at $0.98 versus $10.04 per index task
- 35k to 59k: median output tokens per index task, v4.3 to v5

## What it reports

- New: FrontierCode 2.0 (Cognition's private set of 132 real repository tasks, scored on whether a maintainer would merge the change) and Terminal-Bench-Science (70 agentic research tasks). Upgraded: HLE-Diamond (1,000 questions), AA-Omniscience v1.1, a 40-question hard subset of CritPt v2. Removed: GDPval-AA v2.1, SciCode, AA-LCR v1.1.
- Leaders: Claude Opus 5.5 (max) 58, GPT-6 Astra (max) 57, GPT-6.1 Sol (max) 55, Gemini 4 Argon (high) 50. No model leads all nine evaluations; Opus 5.5 leads three.
- Open weights: Kimi K3.1 Pro, weights not yet released, scores 46; among released weights DeepSeek V4.1 Flash 1017 and GLM-5.3 lead at 32, below Claude Haiku 5.5 (36).
- Cost per index task among the ten highest scorers ranges from $0.96 (GPT-6.1 Terra) to $13.04 (Claude Fable 5.1). GPT-6.1 Sol (max) scores within 4 points of Opus 5.5 at about an eighth of the cost ($1.27 versus $10.04).
- Effort matters more: Opus 5.5 spans 35 to 58 from low to max effort; GPT-6.1 Sol's spread doubles from 10 to 20 points.
- Agentic evaluations are 55% of the weight but a median of about 90% of output tokens.
- Also launched: a Progress Index placing models since November 2022 on one capability scale.

## How they got it

Artificial Analysis runs each benchmark itself, agentic tasks in its own harness (Stirrup), FrontierCode tasks three times each. Weights and methodology are published on its site; most test sets are private.

## Limits

- Weighting and benchmark selection are editorial choices; a different mix would reorder close models.
- Private test sets limit outside replication of scores.
- Results depend on the firm's harness and effort settings, not each vendor's best configuration.
- Kimi K3.1 Pro's open-weight status was prospective at publication.

## Why it matters for companies

The index shows that model choice is now as much about effort setting and cost per task as about ranking: a near-top model at an eighth of the cost, or the top model at low effort scoring far lower, changes the economics of any agent workload. The widening open-weight gap matters to firms planning to self-host: on these tests, released open models sit 26 points behind the leader. Teams should benchmark at the effort level they will actually pay for.

Original: https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v5