모든 평가

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.
예시 작업 보기

AA-3D

Claude Sonnet 5.5 (Max, Default Fallback)이(가) AA-3D에서 1199점으로 가장 높습니다. 그다음은 Claude Opus 5.5 (Max, Default Fallback)(점수 1196) 및 GPT-6 Astra (Max)(점수 1164)입니다

AA-3D 리더보드

범위
모델
Elo
11-2
Anthropic logo
Claude Sonnet 5.5 (Max, Default Fallback)
1199±3395% 구간 1166~1231
21-3
Anthropic logo
Claude Opus 5.5 (Max, Default Fallback)
1196±3395% 구간 1164~1229
33-5
OpenAI logo
GPT-6 Astra (Max)
1164±3295% 구간 1132~1196
43-6
Google logo
Gemini 4 Argon (High)
1153±3395% 구간 1120~1186
53-8
Anthropic logo
Claude Fable 5.1 (Max, Default Fallback)
1135±3295% 구간 1103~1167
64-8
OpenAI logo
GPT-6 Sol (Max)
1121±3295% 구간 1089~1153
75-8
OpenAI logo
GPT-6.1 Sol (Max)
1119±3295% 구간 1088~1151
85-8
Kimi logo
Kimi K3.1 Pro
1110±3395% 구간 1078~1143
99
Meta logo
Muse Spark 1.3 (Max)
1033±3195% 구간 1003~1064
1010
OpenAI logo
GPT-6 Luna (Max)
1000기준이 리더보드 Elo 척도의 기준점으로 1000에 고정되어 있습니다.
1110-14
Anthropic logo
Claude Haiku 5.5 (Max)
998±3195% 구간 967~1028
1210-15
SpaceXAI logo
Grok 4.7 (Xhigh)
995±3195% 구간 964~1025
1310-15
Xiaomi logo
MiMo-V2.6-Pro
983±3195% 구간 952~1014
1410-15
Kimi logo
Kimi K3 (Max)
982±3195% 구간 951~1013
1512-17
Google logo
Gemini 3.8 Flash (High)
966±3195% 구간 935~997
1615-17
Alibaba logo
Qwen3.8 Max (0902)
945±3195% 구간 914~976
1715-17
Z AI logo
GLM-5.3-Flash
937±3195% 구간 906~967
1818-19
Mistral logo
Mistral Large 4 Preview
870±3195% 구간 839~901
1918-19
StepFun logo
Step 5 Preview
854±3295% 구간 821~886
2020
Z AI logo
GLM-5.3 (Max)
783±3495% 구간 749~817
2121
MiniMax logo
MiniMax-M3
740±3395% 구간 707~773
2222-23
Thinking Machines logo
Inkling (Xhigh)
583±4095% 구간 543~623
2322-23
NVIDIA logo
Nemotron 3 Ultra 550B A55B (Reasoning)
573±4395% 구간 529~616

AA-3D Elo

AA-3D Elo

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

AA-3D: Elo vs. 작업당 비용

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

분야, 작업, 모델을 선택하면 해당 모델이 구축한 장면을 불러옵니다.

작업

모델

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

장면을 불러오는 중

블라인드 투표에는 23개 모델 중 23개 모델이 만든 이 작업의 장면이 있습니다.

Elo vs. Intelligence Index

AA-3D Elo vs. Intelligence Index

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

분야별 승률

각 셀은 현재 리더보드의 기반이 되는 투표 중 모델이 한 작업 분야에서 이긴 투표의 비율을 보여 줍니다. 상대 균등 보기는 분야에서 만난 각 상대에 대한 모델의 승률을 평균하므로 모든 상대가 같은 비중으로 계산됩니다.

AA-3D 분야별 승률

각 작업 분야에서 이긴 패널 투표의 비율 · 패널 투표 1.2만건 이상 기준

비용

AA-3D: 작업당 비용

작업당 평균 비용(USD)을 입력, 캐시 적중, 캐시 쓰기, 추론 및 답변 토큰으로 구분

토큰 사용량

AA-3D: 작업당 출력 토큰

작업 하나를 실행하는 데 사용된 출력 토큰을 추론 및 답변 토큰으로 구분

장면당 턴 수

AA-3D 장면당 턴 수

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

도구 사용

AA-3D 장면당 도구 호출

장면당 평균 도구 호출 수, 호출 목적별 분류

AA-3D 작동 방식

작업 설명오프라인 샌드박스결과물Kerr black hole납품 규격시간0분 / 3시간턴 수0 / 250모델code_execview_image자체 렌더링샌드박스의 도구BlenderRapier 물리 엔진Python헤드리스 브라우저CC0 에셋 라이브러리index.html브라우저에서 실행되는인터랙티브 장면

각 모델은 오프라인 샌드박스에서 에이전트로 작업합니다. 하나의 인터랙티브 장면에 대한 서면 작업 설명과 모든 작업에 공통으로 적용되는 납품 규격을 받습니다. 모델은 코드를 작성하고, 장면을 렌더링하고, 자체 스크린샷을 확인한 뒤 수정합니다. Blender, Rapier 물리 엔진, Python, 헤드리스 브라우저, CC0 에셋 라이브러리를 사용할 수 있습니다. 실행은 3시간 또는 250턴이 지나면 종료됩니다. 결과물은 브라우저에서 장면을 실행하는 index.html 페이지입니다.

에이전트 실행 들여다보기

이 리플레이는 AA-3D 출시 시점의 데이터를 사용합니다. 작업을 선택하면 각 모델의 실행을 공통 시계로 재생합니다. 레인에 마우스를 올리면 각 동작을 볼 수 있고, 클릭하면 시간 사용 내역을 볼 수 있습니다.

장면이 구축되는 과정 보기

점수 vs. 출시일

AA-3D: 점수 vs. 출시일

Most attractive region

자주 묻는 질문

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