AA-3D: Interactive 3D Experience Benchmark
AA-3D
Clasificación de AA-3D
Rango | Modelo | Elo | |
|---|---|---|---|
| 1 | 1-3 |  Claude Sonnet 5.5 (Max, Default Fallback) | 1194±34Intervalo del 95%: 1160 a 1228 |
| 2 | 1-3 | Claude Opus 5.5 (Max, Default Fallback) | 1190±35Intervalo del 95%: 1155 a 1225 |
| 3 | 1-4 |  GPT-6 Astra (Max) | 1165±34Intervalo del 95%: 1131 a 1199 |
| 4 | 4-6 | Claude Fable 5.1 (Max, Default Fallback) | 1131±34Intervalo del 95%: 1098 a 1165 |
| 5 | 4-6 | GPT-6 Sol (Max) | 1123±34Intervalo del 95%: 1089 a 1156 |
| 6 | 4-6 | GPT-6.1 Sol (Max) | 1114±33Intervalo del 95%: 1081 a 1148 |
| 7 | 7 |  Muse Spark 1.3 (Max) | 1035±32Intervalo del 95%: 1003 a 1067 |
| 8 | 8 | GPT-6 Luna (Max) | 1000AnclaFijado en 1000 como punto de referencia de la escala Elo de esta clasificación. |
| 9 | 8-13 | Claude Haiku 5.5 (Max) | 994±32Intervalo del 95%: 962 a 1026 |
| 10 | 8-13 |  Grok 4.7 (Xhigh) | 994±32Intervalo del 95%: 962 a 1026 |
| 11 | 8-13 |  Kimi K3 (Max) | 982±33Intervalo del 95%: 949 a 1015 |
| 12 | 8-14 |  MiMo-V2.6-Pro | 977±32Intervalo del 95%: 945 a 1009 |
| 13 | 9-14 |  Gemini 3.8 Flash (High) | 965±32Intervalo del 95%: 932 a 997 |
| 14 | 12-15 |  Qwen3.8 Max (0902) | 948±32Intervalo del 95%: 916 a 981 |
| 15 | 14-15 |  GLM-5.3-Flash | 931±32Intervalo del 95%: 899 a 963 |
| 16 | 16-17 |  Mistral Large 4 Preview | 868±32Intervalo del 95%: 835 a 900 |
| 17 | 16-17 |  Step 5 Preview | 848±33Intervalo del 95%: 814 a 881 |
| 18 | 18 | GLM-5.3 (Max) | 782±35Intervalo del 95%: 747 a 816 |
| 19 | 19 |  MiniMax-M3 | 739±34Intervalo del 95%: 704 a 773 |
| 20 | 20-21 |  Inkling (Xhigh) | 579±41Intervalo del 95%: 538 a 620 |
| 21 | 20-21 |  Nemotron 3 Ultra 550B A55B (Reasoning) | 569±45Intervalo del 95%: 524 a 614 |
AA-3D Elo
AA-3D Elo
AA-3D: Elo vs. costo por tarea
Tareas de ejemplo
Elige un dominio, una tarea y un modelo para cargar la escena que creó ese modelo.
Tarea
Modelo
Create an interactive adult man performing weightlifting exercises, with views that expose his muscles, skeleton and organs in anatomically accurate form.
21 de 21 modelos tienen una escena para esta tarea en la votación a ciegas.
Elo vs. Intelligence Index
Elo de AA-3D vs. Intelligence Index
Tasa de victorias por dominio
Cada celda muestra la proporción de votos que un modelo ganó en un dominio de tareas, a partir de los votos de la clasificación actual. La vista equilibrada por rival promedia las tasas de victorias del modelo contra cada rival del dominio, para que cada rival cuente por igual.
Tasa de victorias de AA-3D por dominio
Costo
AA-3D: costo por tarea
Uso de tokens
AA-3D: tokens de salida por tarea
Turnos por escena
Turnos por escena de AA-3D
Uso de herramientas
Llamadas a herramientas por escena de AA-3D
Cómo funciona AA-3D
Cada modelo trabaja como agente en un sandbox sin conexión. Recibe una descripción escrita de una escena interactiva y un contrato de entrega que comparten todas las tareas. El modelo escribe código, renderiza la escena, revisa sus propias capturas de pantalla y hace correcciones. Tiene a su disposición Blender, física Rapier, Python, un navegador headless y una biblioteca de recursos CC0. Una ejecución se detiene a las 3 horas o tras 250 turnos. El resultado es una página index.html que ejecuta la escena en un navegador.
Dentro de una ejecución del agente
Estas repeticiones usan los datos del lanzamiento de AA-3D. Elige una tarea para reproducir la ejecución de cada modelo con un reloj compartido. Pasa el cursor sobre una fila para ver cada acción y haz clic en ella para ver en qué se fue el tiempo.
Mira cómo se crea una escena
Puntuación vs. fecha de lanzamiento
AA-3D: puntuación vs. fecha de lanzamiento
Preguntas frecuentes
AA-3D v1.0 mide qué tan bien los modelos de IA crean experiencias 3D interactivas. Cada modelo recibe una descripción escrita de la tarea y crea una sola página web con three.js en un sandbox sin conexión. El benchmark tiene 55 tareas en 7 dominios. Lee la metodología de AA-3D
Los modelos se clasifican con un sistema de calificación Elo derivado de comparaciones a ciegas realizadas por nuestro panel humano reclutado. Las personas del panel interactúan con dos escenas creadas para la misma tarea y eligen la que prefieren, sin saber qué modelo creó cada una. Un Elo más alto indica que un modelo se prefiere con más frecuencia. Prueba la Votación de AA-3D
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