Vibe-Bench - BERT Retrieval - Preference Accuracy: leaderboard
Vibe-Bench tests personalization from user histories: selecting an emotion-regulation strategy, generating a personalized response, and predicting occupational preferences. The paper separates no-history, full-history, profile, retrieval and personalized fine-tuning protocols. P-SFT checkpoints retain distinct identities from their untrained backbones. This series reports Vibe-Bench - BERT Retrieval - Preference Accuracy from 2609.00921v1, A11.T11: Score (0-100). Higher is better.
Metric: Score (0-100). Source: arxiv.org. Saturation forecast: Estimated already saturated. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3 235B A22B 2507 Instruct | 64.06 | #321 |
| 2 | Llama 3.1 70B Instruct | 56.25 | #570 |
| 3 | Mistral Small 3.2 | 56.25 | #610 |
| 4 | Qwen 3 14B | 55.47 | #542 |
| 5 | Llama 3.1 8B Instruct | 53.12 | #1021 |
| 6 | Gemma 3 27B (IT) | 49.22 | #530 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #6 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=vibe-bench-bert-retrieval-preference-accuracy · How It Works · Data refreshed daily, snapshot 2026-11-07.