AA-3D: Interactive 3D Experience Benchmark
AA-3D
AA-3Dリーダーボード
順位範囲 | モデル | Elo | |
|---|---|---|---|
| 1 | 1-3 | Claude Sonnet 5.5 (Max, Default Fallback) | 1194±3495%区間 1160〜1228 |
| 2 | 1-3 | Claude Opus 5.5 (Max, Default Fallback) | 1190±3595%区間 1155〜1225 |
| 3 | 1-4 | GPT-6 Astra (Max) | 1165±3495%区間 1131〜1199 |
| 4 | 4-6 | Claude Fable 5.1 (Max, Default Fallback) | 1131±3495%区間 1098〜1165 |
| 5 | 4-6 | GPT-6 Sol (Max) | 1123±3495%区間 1089〜1156 |
| 6 | 4-6 | GPT-6.1 Sol (Max) | 1114±3395%区間 1081〜1148 |
| 7 | 7 | Muse Spark 1.3 (Max) | 1035±3295%区間 1003〜1067 |
| 8 | 8 | GPT-6 Luna (Max) | 1000基準このリーダーボードのEloスケールの基準点として1000に固定されています。 |
| 9 | 8-13 | Claude Haiku 5.5 (Max) | 994±3295%区間 962〜1026 |
| 10 | 8-13 | Grok 4.7 (Xhigh) | 994±3295%区間 962〜1026 |
| 11 | 8-13 | Kimi K3 (Max) | 982±3395%区間 949〜1015 |
| 12 | 8-14 | MiMo-V2.6-Pro | 977±3295%区間 945〜1009 |
| 13 | 9-14 | Gemini 3.8 Flash (High) | 965±3295%区間 932〜997 |
| 14 | 12-15 | Qwen3.8 Max (0902) | 948±3295%区間 916〜981 |
| 15 | 14-15 | GLM-5.3-Flash | 931±3295%区間 899〜963 |
| 16 | 16-17 | Mistral Large 4 Preview | 868±3295%区間 835〜900 |
| 17 | 16-17 | Step 5 Preview | 848±3395%区間 814〜881 |
| 18 | 18 | GLM-5.3 (Max) | 782±3595%区間 747〜816 |
| 19 | 19 | MiniMax-M3 | 739±3495%区間 704〜773 |
| 20 | 20-21 | Inkling (Xhigh) | 579±4195%区間 538〜620 |
| 21 | 20-21 | Nemotron 3 Ultra 550B A55B (Reasoning) | 569±4595%区間 524〜614 |
AA-3D Elo
AA-3D Elo
AA-3D:Elo vs. タスクあたりのコスト
タスク例
分野、タスク、モデルを選ぶと、そのモデルが構築したシーンを読み込みます。
タスク
モデル
Create an interactive adult man performing weightlifting exercises, with views that expose his muscles, skeleton and organs in anatomically accurate form.
このタスクには、21モデル中21モデルのシーンがブラインド投票に含まれています。
Elo vs. Intelligence Index
AA-3D Elo vs. Intelligence Index
分野別勝率
各セルは、現在のリーダーボードの基になっている投票のうち、モデルが1つのタスク分野で勝った割合を示します。対戦相手で均等化した表示では、分野内の各対戦相手に対するモデルの勝率を平均するため、すべての対戦相手が同じ重みで数えられます。
AA-3Dの分野別勝率
コスト
AA-3D:タスクあたりのコスト
トークン使用量
AA-3D:タスクあたりの出力トークン
シーンあたりのターン数
AA-3Dのシーンあたりのターン数
ツールの使用
AA-3Dのシーンあたりのツール呼び出し
AA-3Dの仕組み
各モデルは、オフラインのサンドボックス内でエージェントとして作業します。1つのインタラクティブなシーンについて書かれたタスク説明と、全タスク共通の納品仕様を受け取ります。モデルはコードを書き、シーンをレンダリングし、自分のスクリーンショットを確認して修正します。Blender、Rapier物理エンジン、Python、ヘッドレスブラウザ、CC0アセットのライブラリを利用できます。実行は3時間または250ターンで終了します。成果物は、ブラウザでシーンを実行するindex.htmlページです。
エージェント実行の詳細
これらのリプレイは、AA-3Dのリリース時のデータを使用しています。タスクを選ぶと、各モデルの実行を共通の時間軸で再生します。レーンにカーソルを合わせると各アクションが表示され、クリックすると時間の内訳が表示されます。
シーンが構築される様子を見る
スコア vs. リリース日
AA-3D:スコア vs. リリース日
よくある質問
AA-3D v1.0は、AIモデルがインタラクティブな3D体験をどれだけうまく構築できるかを測定します。各モデルは書面のタスク説明を受け取り、オフラインのサンドボックス内でthree.jsを使って単一のWebページを構築します。このベンチマークは7つの分野にわたる55のタスクで構成されています。 AA-3Dの方法論を読む
モデルは、当社が募集した人間の評価者パネルによるブラインド比較から導出されたElo評価システムでランク付けされます。パネル参加者は同じタスク向けに構築された2つのシーンを操作し、どのモデルがどちらを構築したかを知らないまま、好みのシーンを選びます。Eloスコアが高いほど、そのモデルがより頻繁に好まれることを示します。 AA-3D投票に参加する
評価を探す
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