BehaviorBench (Behavioral Traces) - Trade Amount Error (ProfileGen): leaderboard

BehaviorBench (Yang et al., 2026) tests whether a model can predict what a real pseudonymous user will do, using 2,000 wallets reconstructed from public prediction-market and on-chain records with chronologically held-out targets. The model sees a structured profile summarizing the past behavior of the wallet (ProfileGen). The board is the median absolute error of the predicted transaction amount, in paper units and unbounded; lower is better.

Metric: Median absolute error of the predicted trade amount (paper units, unbounded); history interface ProfileGen (a released structured profile summarizing the wallet's pre-target behavior); 2,000 pseudonymous prediction-market wallets reconstructed from public Polymarket metadata and Polygon on-chain logs, about 59,560 chronologically held-out test transactions; unparsable outputs count as failures; context-only baseline 31.79; lower is better. Source: arxiv.org. Saturation forecast: Not forecast. 11 models tracked.

Top models

#ModelScoreOverall rank
1Llama 4 Scout Instruct10.3#1543
2Gemma 3 12B (IT)10.99#1837
3GPT-5.4 Mini12.96#389
4Claude 3.7 Sonnet14.12#458
5GPT-5.414.61#123
6Gemma 3 27B (IT)15.18#1289
7Claude Opus 415.35#322
8GPT-OSS-20B15.5#1147
9NVIDIA-Nemotron-3-Nano-30B-A3B-BF1617.07#1767
10GPT-5.4 Nano20.83#635
11Ministral-3-14B-Instruct-251235.48#1593

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=behaviorbench-behavioral-traces-trade-amount-error-profilegen · How It Works · Data refreshed daily, snapshot 2026-11-13.