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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.
Ver tarefas de exemplo

AA-3D

Claude Sonnet 5.5 (Max, Default Fallback) alcança a maior pontuação em AA-3D, com 1199, seguido por Claude Opus 5.5 (Max, Default Fallback), com pontuação de 1196 e GPT-6 Astra (Max), com pontuação de 1164

Ranking do AA-3D

Faixa
Modelo
Elo
11-2
Anthropic logo
Claude Sonnet 5.5 (Max, Default Fallback)
1199±33Intervalo de 95%: 1166 a 1231
21-3
Anthropic logo
Claude Opus 5.5 (Max, Default Fallback)
1196±33Intervalo de 95%: 1164 a 1229
33-5
OpenAI logo
GPT-6 Astra (Max)
1164±32Intervalo de 95%: 1132 a 1196
43-6
Google logo
Gemini 4 Argon (High)
1153±33Intervalo de 95%: 1120 a 1186
53-8
Anthropic logo
Claude Fable 5.1 (Max, Default Fallback)
1135±32Intervalo de 95%: 1103 a 1167
64-8
OpenAI logo
GPT-6 Sol (Max)
1121±32Intervalo de 95%: 1089 a 1153
75-8
OpenAI logo
GPT-6.1 Sol (Max)
1119±32Intervalo de 95%: 1088 a 1151
85-8
Kimi logo
Kimi K3.1 Pro
1110±33Intervalo de 95%: 1078 a 1143
99
Meta logo
Muse Spark 1.3 (Max)
1033±31Intervalo de 95%: 1003 a 1064
1010
OpenAI logo
GPT-6 Luna (Max)
1000ÂncoraFixado em 1000 como ponto de referência da escala Elo deste ranking.
1110-14
Anthropic logo
Claude Haiku 5.5 (Max)
998±31Intervalo de 95%: 967 a 1028
1210-15
SpaceXAI logo
Grok 4.7 (Xhigh)
995±31Intervalo de 95%: 964 a 1025
1310-15
Xiaomi logo
MiMo-V2.6-Pro
983±31Intervalo de 95%: 952 a 1014
1410-15
Kimi logo
Kimi K3 (Max)
982±31Intervalo de 95%: 951 a 1013
1512-17
Google logo
Gemini 3.8 Flash (High)
966±31Intervalo de 95%: 935 a 997
1615-17
Alibaba logo
Qwen3.8 Max (0902)
945±31Intervalo de 95%: 914 a 976
1715-17
Z AI logo
GLM-5.3-Flash
937±31Intervalo de 95%: 906 a 967
1818-19
Mistral logo
Mistral Large 4 Preview
870±31Intervalo de 95%: 839 a 901
1918-19
StepFun logo
Step 5 Preview
854±32Intervalo de 95%: 821 a 886
2020
Z AI logo
GLM-5.3 (Max)
783±34Intervalo de 95%: 749 a 817
2121
MiniMax logo
MiniMax-M3
740±33Intervalo de 95%: 707 a 773
2222-23
Thinking Machines logo
Inkling (Xhigh)
583±40Intervalo de 95%: 543 a 623
2322-23
NVIDIA logo
Nemotron 3 Ultra 550B A55B (Reasoning)
573±43Intervalo de 95%: 529 a 616

AA-3D Elo

AA-3D Elo

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

AA-3D: Elo vs. custo por tarefa

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

Escolha um domínio, uma tarefa e um modelo para carregar a cena que esse modelo criou.

Tarefa

Modelo

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

Carregando a cena

23 de 23 modelos têm uma cena para esta tarefa na votação cega.

Elo vs. Intelligence Index

Elo do AA-3D vs. Intelligence Index

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

Taxa de vitórias por domínio

Cada célula mostra a parcela dos votos que um modelo venceu em um domínio de tarefas, com base nos votos do ranking atual. A visão equilibrada por adversário tira a média das taxas de vitórias do modelo contra cada adversário do domínio, para que cada adversário conte igualmente.

Taxa de vitórias do AA-3D por domínio

Parcela dos votos do painel vencidos em cada domínio de tarefas · Com base em mais de 12 mil votos do painel

Custo

AA-3D: custo por tarefa

Custo médio por tarefa (USD), detalhado em tokens de entrada, acertos e gravações de cache, raciocínio e resposta

Uso de tokens

AA-3D: tokens de saída por tarefa

Tokens de saída usados para executar uma tarefa, detalhados em tokens de raciocínio e resposta

Turnos por cena

Turnos por cena do AA-3D

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

Uso de ferramentas

Chamadas de ferramentas por cena do AA-3D

Média de chamadas de ferramentas por cena, de acordo com o que cada chamada faz

Como o AA-3D funciona

DescriçãoSandbox offlineResultadoKerr black holeContrato de entregaTempo0 min de 3 hTurnos0 de 250Modelocode_execview_imageImagem própriaFerramentas no sandboxBlenderFísica RapierPythonNavegador headlessBiblioteca de recursos CC0index.htmlCena interativano navegador

Cada modelo trabalha como agente em um sandbox offline. Ele recebe uma descrição escrita de uma cena interativa e um contrato de entrega comum a todas as tarefas. O modelo escreve código, renderiza a cena, analisa as próprias capturas de tela e faz revisões. Ele tem à disposição Blender, física Rapier, Python, um navegador headless e uma biblioteca de recursos CC0. Uma execução termina após 3 horas ou 250 turnos. O resultado é uma página index.html que executa a cena em um navegador.

Por dentro de uma execução do agente

Estas reproduções usam os dados do lançamento do AA-3D. Escolha uma tarefa para reproduzir a execução de cada modelo em um relógio compartilhado. Passe o cursor sobre uma linha para ver cada ação e clique nela para ver para onde foi o tempo.

Veja uma cena sendo criada

Pontuação vs. data de lançamento

AA-3D: pontuação vs. data de lançamento

Most attractive region

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