すべての評価

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.
タスク例を見る

AA-3D

Claude Sonnet 5.5 (Max, Default Fallback)がAA-3Dで最高スコア1199を獲得。次いでClaude Opus 5.5 (Max, Default Fallback)(スコア:1196)、GPT-6 Astra (Max)(スコア:1164)

AA-3Dリーダーボード

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

AA-3D Elo

AA-3D Elo

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

AA-3D:Elo vs. タスクあたりのコスト

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

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.

シーンを読み込んでいます

このタスクには、23モデル中23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

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

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

各タスク分野でパネル投票に勝った割合 · パネル投票1.2万件以上に基づく

コスト

AA-3D:タスクあたりのコスト

タスクあたりの平均コスト(USD)。入力、キャッシュヒット、キャッシュ書き込み、推論、回答トークン別

トークン使用量

AA-3D:タスクあたりの出力トークン

1タスクの実行に使用した出力トークン(推論トークンと回答トークンの内訳)

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

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

ツールの使用

AA-3Dのシーンあたりのツール呼び出し

シーンあたりの平均ツール呼び出し回数(呼び出しの目的別)

AA-3Dの仕組み

タスク説明オフラインのサンドボックス成果物Kerr black hole納品仕様時間0分 / 3時間ターン数0 / 250モデルcode_execview_image自作の画像サンドボックス内のツールBlenderRapier物理エンジンPythonヘッドレスブラウザCC0アセットライブラリindex.htmlブラウザで動く対話型のシーン

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

Most attractive region

よくある質問

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