AA-3D: Interactive 3D Experience Benchmark
AA-3D
AA-3D Leaderboard
Range | Model | Elo | |
|---|---|---|---|
| 1 | 1-3 |  Claude Sonnet 5.5 (Max, Default Fallback) | 1194±3495% interval 1160 to 1228 |
| 2 | 1-3 | Claude Opus 5.5 (Max, Default Fallback) | 1190±3595% interval 1155 to 1225 |
| 3 | 1-4 |  GPT-6 Astra (Max) | 1165±3495% interval 1131 to 1199 |
| 4 | 4-6 | Claude Fable 5.1 (Max, Default Fallback) | 1131±3495% interval 1098 to 1165 |
| 5 | 4-6 | GPT-6 Sol (Max) | 1123±3495% interval 1089 to 1156 |
| 6 | 4-6 | GPT-6.1 Sol (Max) | 1114±3395% interval 1081 to 1148 |
| 7 | 7 |  Muse Spark 1.3 (Max) | 1035±3295% interval 1003 to 1067 |
| 8 | 8 | GPT-6 Luna (Max) | 1000AnchorFixed at 1000 as the reference point for this board's Elo scale. |
| 9 | 8-13 | Claude Haiku 5.5 (Max) | 994±3295% interval 962 to 1026 |
| 10 | 8-13 |  Grok 4.7 (Xhigh) | 994±3295% interval 962 to 1026 |
| 11 | 8-13 |  Kimi K3 (Max) | 982±3395% interval 949 to 1015 |
| 12 | 8-14 |  MiMo-V2.6-Pro | 977±3295% interval 945 to 1009 |
| 13 | 9-14 |  Gemini 3.8 Flash (High) | 965±3295% interval 932 to 997 |
| 14 | 12-15 |  Qwen3.8 Max (0902) | 948±3295% interval 916 to 981 |
| 15 | 14-15 |  GLM-5.3-Flash | 931±3295% interval 899 to 963 |
| 16 | 16-17 |  Mistral Large 4 Preview | 868±3295% interval 835 to 900 |
| 17 | 16-17 |  Step 5 Preview | 848±3395% interval 814 to 881 |
| 18 | 18 | GLM-5.3 (Max) | 782±3595% interval 747 to 816 |
| 19 | 19 |  MiniMax-M3 | 739±3495% interval 704 to 773 |
| 20 | 20-21 |  Inkling (Xhigh) | 579±4195% interval 538 to 620 |
| 21 | 20-21 |  Nemotron 3 Ultra 550B A55B (Reasoning) | 569±4595% interval 524 to 614 |
AA-3D Elo
AA-3D Elo
AA-3D: Elo vs. Cost per Task
Example Tasks
Pick a domain, a task and a model to load the scene that model built.
Task
Model
Create an interactive adult man performing weightlifting exercises, with views that expose his muscles, skeleton and organs in anatomically accurate form.
21 of 21 models have a scene for this task in blind voting.
Elo vs. Intelligence Index
AA-3D Elo vs. Intelligence Index
Win Rate by Domain
Each cell shows the share of votes a model won in one task domain, from the votes behind the current leaderboard. The opponent-balanced view takes the mean of the model's win rates against each opponent in the domain, so every opponent counts equally.
AA-3D Win Rate by Domain
Cost
AA-3D: Cost per Task
Token Usage
AA-3D: Output Tokens per Task
Turns per Scene
AA-3D Turns per Scene
Tool Use
AA-3D Tool Calls per Scene
How AA-3D Works
Each model works as an agent in an offline sandbox. It gets a written brief for one interactive scene, plus a delivery contract that every task shares. It writes code, renders the scene, looks at its own screenshots and revises. The sandbox has Blender, Rapier physics, Python, a headless browser and a library of CC0 assets. A run stops after three hours or 250 turns, and the result is an index.html page that runs the scene in a browser.
Inside an Agent Run
These replays use the AA-3D launch data. Pick a task to replay every model's run on a shared clock. Hover over a lane to see each action, and click it to see where the time went.
Watch a Scene Being Built
Score vs. Release Date
AA-3D: Score vs. Release Date
Frequently Asked Questions
AA-3D v1.0 measures how well AI models build interactive 3D experiences. Each model receives a written brief and builds a single web page with three.js in an offline sandbox. The benchmark has 55 tasks across 7 domains. Read the AA-3D methodology
Models are ranked using an Elo rating system derived from blind comparisons judged by our recruited human panel. Panelists interact with two scenes built for the same task and choose the one they prefer, without knowing which model built which. Higher Elo scores indicate a model is preferred more often. Try AA-3D Voting
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