RPCBench - Domain - MovieLens-1M - Conditional Strategy Quality: leaderboard
RPCBench tests proactive premise critique in recommendation requests. This series measures conditional strategy quality on the MovieLens-1M subset (462 questions), using the same eleven-model, three-judge experiment as the headline paper. Published CSQ fractions from the authors' per-model CSV are scaled once to Strategy Quality (0-100). Higher is better; domains and nested premise-error groups are related slices of the same experiment.
Metric: Strategy Quality (0-100). Source: github.com. Saturation forecast: Around February 2027. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3.5 Plus | 88.36 | #232 |
| 2 | DeepSeek V4 Pro | 86.56 | #188 |
| 3 | Qwen 3.5 397B A17B | 85.39 | #245 |
| 4 | Qwen 3.5 122B A10B | 85.27 | #335 |
| 5 | Claude Sonnet 4.6 | 82.07 | #159 |
| 6 | DeepSeek V4 Flash | 80.93 | #259 |
| 7 | Qwen 3.5 35B A3B | 79.49 | #492 |
| 8 | GPT-5.5 | 75.38 | #48 |
| 9 | Gemini 3.1 Pro (Preview) | 75.32 | #93 |
| 10 | Llama 3.1 70B Instruct | 51.28 | #1629 |
| 11 | Llama 3.1 8B Instruct | 48.08 | #4318 |
No result here: #4 Claude Opus 5.5, #5 GPT-6 Astra, #9 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=rpcbench-domain-movielens-1m-conditional-strategy-quality · How It Works · Data refreshed daily, snapshot 2026-11-08.