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 1194점으로 가장 높습니다. 그다음은 Claude Opus 5.5 (Max, Default Fallback)(점수 1190) 및 GPT-6 Astra (Max)(점수 1165)입니다

AA-3D 리더보드

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

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) · Lower is better
Most attractive quadrant
Pareto line

예시 작업

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 21개 모델 중 21개 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 1만건 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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