# Artificial Analysis Intelligence Index v4.1 发布：转向智能体任务评测

- 来源：Artificial Analysis (@ArtificialAnlys)
- 发布时间：2026-06-16 09:51
- AIHOT 分数：60
- AIHOT 链接：https://aihot.virxact.com/items/cmqg0ltah02frslspvfe0exw3
- 原文链接：https://x.com/ArtificialAnlys/status/2066700136018071841

## AI 摘要

Artificial Analysis 发布 Intelligence Index v4.1，转向智能体任务。升级 Terminal-Bench 2.1、τ³-Bench Banking、GDPval-AA v2（Elo 重基线、引入前沿模型评审、回合上限增至250），移除饱和的 IFBench。新增每任务成本、时间、输出 token 指标及缓存 token 影响。关键结果：Claude Fable 5（60分）领先但不可用；可用模型中 Claude Opus 4.8（max）56分居首，GPT-5.5（xhigh）55分。开源 DeepSeek V4 Pro 与 MiniMax M3 均44分。成本方面，Opus 4.8 每任务 $1.78，GPT-5.5 $0.99，DeepSeek V4 Pro 仅 $0.04。时间方面，Grok 4.3 最快（1.5分钟），Opus 4.8 需6.4分钟，GPT-5.5 需3.7分钟，Gemini 3.1 Pro Preview 以1.6分钟得46分。

## 正文

Announcing Artificial Analysis Intelligence Index v4.1： a shift toward agentic workloads， featuring upgraded benchmarks and new per-task metrics

The Artificial Analysis Intelligence Index is our synthesis metric for assessing model intelligence and tracking AI progress. v4.1 marks a broader shift toward agentic workloads， with three main changes：

Updated and reweighted evaluations toward agentic tasks：
1. We upgraded three evaluations， removed one， and reweighted the Intelligence Index：
➤ Upgraded Terminal-Bench Hard to Terminal-Bench 2.1 and τ2-Bench Telecom to τ3-Bench Banking. Both move to newer， more robust task sets with harder， more realistic agentic scenarios that better separate frontier models
➤ Upgraded GDPval-AA to GDPval-AA v2. The upgrade re-baselines Elo to human performance at 1000， introduces a rotating panel of frontier-model judges， and raises the turn limit from 100 to 250 for longer-horizon agent trajectories
➤ Removed IFBench due to saturation. The benchmark no longer distinguishes frontier models sufficiently， so we have removed it from the Intelligence Index. We will continue to run it and publish results on new model releases

2. Cost per Task， Time per Task， and Tokens per Task：
Three new per-task metrics， reported for every model and based on the Intelligence Index. We take the total cost， total time， and total output tokens for a model to run the Intelligence Index and divide by the number of tasks across its evaluations， giving the average cost， time， and output tokens to complete a single Intelligence Index task

3. Cached input token reporting：
We now report cached input tokens and their impact on cost， including the cost to run the Intelligence Index， to better reflect the real cost of running each model

Key Results：
➤ Leading models： Claude Fable 5 （with Opus 4.8 fallback， 60） leads the Artificial Analysis Intelligence Index v4.1 by four points but is currently unavailable， leaving Claude Opus 4.8 （max， 56） as the most intelligent available model， ahead of GPT-5.5 （xhigh， 55） ➤ Open weights leading models： Among open weights models， DeepSeek V4 Pro （max， 44） and MiniMax M3 （44） lead， followed by Kimi K2.6 （43） and MiMo-V2.5-Pro （42）
➤Cost per Task： Claude Opus 4.8 （max） is the most expensive available model at $1.78 per task， with Claude Fable 5 the highest overall at $3.25. GPT-5.5 （xhigh） scores within a point of Opus 4.8 on the Intelligence Index at $0.99 per task. DeepSeek V4 Pro （max） stands out on the Intelligence vs Cost per Task chart at $0.04 per task， with other leading proprietary models costing 20x to 45x more
➤Time per Task： time per task （inference decode time） ranges from 1.5 minutes for Grok 4.3 （high） to 13.5 for Claude Sonnet 4.6 （max）， a roughly 9x spread. Claude Opus 4.8 （max） completes a task in 6.4 minutes and GPT-5.5 （xhigh） in 3.7， while Gemini 3.1 Pro Preview stands out on the Intelligence vs Time per Task chart at 1.6 minutes for a score of 46