所有评测

AA-3D: Interactive 3D Experience Benchmark

A benchmark developed by Artificial Analysis for how well AI models build interactive 3D experiences. Models work as agents in an offline sandbox via Stirrup to turn a written brief into a three.js scene, with Elo ratings derived from blind pairwise votes.
查看示例任务

AA-3D

Claude Sonnet 5.5 (Max, Default Fallback)在 AA-3D 中得分最高,为 1194;其次是Claude Opus 5.5 (Max, Default Fallback),得分 1190和GPT-6 Astra (Max),得分 1165

AA-3D排行榜

范围
模型
Elo
11-3
Anthropic logo
Claude Sonnet 5.5 (Max, Default Fallback)
1194±3495% 区间 1160 至 1228
21-3
Anthropic logo
Claude Opus 5.5 (Max, Default Fallback)
1190±3595% 区间 1155 至 1225
31-4
OpenAI logo
GPT-6 Astra (Max)
1165±3495% 区间 1131 至 1199
44-6
Anthropic logo
Claude Fable 5.1 (Max, Default Fallback)
1131±3495% 区间 1098 至 1165
54-6
OpenAI logo
GPT-6 Sol (Max)
1123±3495% 区间 1089 至 1156
64-6
OpenAI logo
GPT-6.1 Sol (Max)
1114±3395% 区间 1081 至 1148
77
Meta logo
Muse Spark 1.3 (Max)
1035±3295% 区间 1003 至 1067
88
OpenAI logo
GPT-6 Luna (Max)
1000锚点固定为 1000,作为本榜单 Elo 刻度的参照点。
98-13
Anthropic logo
Claude Haiku 5.5 (Max)
994±3295% 区间 962 至 1026
108-13
SpaceXAI logo
Grok 4.7 (Xhigh)
994±3295% 区间 962 至 1026
118-13
Kimi logo
Kimi K3 (Max)
982±3395% 区间 949 至 1015
128-14
Xiaomi logo
MiMo-V2.6-Pro
977±3295% 区间 945 至 1009
139-14
Google logo
Gemini 3.8 Flash (High)
965±3295% 区间 932 至 997
1412-15
Alibaba logo
Qwen3.8 Max (0902)
948±3295% 区间 916 至 981
1514-15
Z AI logo
GLM-5.3-Flash
931±3295% 区间 899 至 963
1616-17
Mistral logo
Mistral Large 4 Preview
868±3295% 区间 835 至 900
1716-17
StepFun logo
Step 5 Preview
848±3395% 区间 814 至 881
1818
Z AI logo
GLM-5.3 (Max)
782±3595% 区间 747 至 816
1919
MiniMax logo
MiniMax-M3
739±3495% 区间 704 至 773
2020-21
Thinking Machines logo
Inkling (Xhigh)
579±4195% 区间 538 至 620
2120-21
NVIDIA logo
Nemotron 3 Ultra 550B A55B (Reasoning)
569±4595% 区间 524 至 614

AA-3D Elo

AA-3D Elo

Elo from blind pairwise votes, with 95% confidence intervals · Higher is better

AA-3D:Elo vs. 每项任务成本

AA-3D Elo vs. average cost per task (USD) · Lower is better
Most attractive quadrant
Pareto line

示例任务

选择一个领域、一项任务和一个模型,即可加载该模型构建的场景。

任务

模型

Create an interactive adult man performing weightlifting exercises, with views that expose his muscles, skeleton and organs in anatomically accurate form.

正在加载场景

21 个模型中有 21 个在盲测投票中有此任务的场景。

Elo vs. Intelligence Index

AA-3D Elo vs. Intelligence Index

AA-3D Elo · Artificial Analysis Intelligence Index
Most attractive quadrant

各领域胜率

每个单元格显示模型在某个任务领域中赢得的投票占比,数据来自当前排行榜所依据的投票。按对手均衡的视图对模型在该领域中对每个对手的胜率取平均值,使每个对手的权重相同。

AA-3D 各领域胜率

在每个任务领域中赢得的评审面板投票占比 · 基于超过 1万 张评审面板投票

成本

AA-3D:每项任务成本

每项任务的平均成本(美元),按输入、缓存命中、缓存写入、推理和回答 Token 划分

Token 使用量

AA-3D:每项任务的输出 Token

运行一项任务使用的输出 Token,按推理和回答 Token 划分

每个场景轮次

AA-3D 每个场景轮次

Distribution of agent turns per scene, up to the limit of 250 · 5th percentile, 25th percentile, median, 75th percentile, 95th percentile · Lower is better

工具使用

AA-3D 每个场景工具调用次数

每个场景的平均工具调用次数,按调用用途分组

AA-3D 的运作方式

任务说明离线沙箱输出Kerr black hole交付规范时间0 分 / 3 小时轮次0 / 250模型code_execview_image自己的渲染图沙箱中的工具BlenderRapier 物理引擎Python无头浏览器CC0 资源库index.html在浏览器中运行的交互式场景

每个模型都在离线沙箱中作为智能体工作。它会收到一份针对一个交互式场景的书面任务说明,以及所有任务共用的交付规范。模型编写代码、渲染场景、查看自己的截图并加以修改。可用的工具包括 Blender、Rapier 物理引擎、Python、无头浏览器和 CC0 资源库。每次运行在 3 小时或 250 轮后停止。最终结果是一个在浏览器中运行该场景的 index.html 页面。

深入了解一次智能体运行

这些回放使用 AA-3D 发布时的数据。选择一项任务,即可在共享时钟上回放每个模型的运行。将鼠标悬停在某一行上可查看每个操作,点击该行可查看时间分配。

观看场景的构建过程

得分 vs. 发布日期

AA-3D:得分 vs. 发布日期

Most attractive region

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