RPCBench - Error Group - Unverifiable Premise - Proactive Detection: leaderboard
RPCBench tests proactive premise critique in recommendation requests. This series measures proactive detection on the Unverifiable Premise subset (1,020 questions), using the same eleven-model, three-judge experiment as the headline paper. Published PDR fractions from the authors' per-model CSV are scaled once to Detection Rate (%). Higher is better; domains and nested premise-error groups are related slices of the same experiment.
Metric: Detection Rate (%). Source: github.com. Saturation forecast: Estimated already saturated. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.5 | 92.55 | #48 |
| 2 | DeepSeek V4 Pro | 91.08 | #188 |
| 3 | Gemini 3.1 Pro (Preview) | 89.61 | #93 |
| 4 | Qwen 3.5 122B A10B | 87.35 | #335 |
| 5 | DeepSeek V4 Flash | 84.71 | #259 |
| 6 | Qwen 3.5 35B A3B | 83.82 | #492 |
| 7 | Qwen 3.5 397B A17B | 82.55 | #245 |
| 8 | Qwen 3.5 Plus | 82.16 | #232 |
| 9 | Claude Sonnet 4.6 | 74.22 | #159 |
| 10 | Llama 3.1 70B Instruct | 41.57 | #1629 |
| 11 | Llama 3.1 8B Instruct | 35.69 | #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-error-group-unverifiable-premise-proactive-detection · How It Works · Data refreshed daily, snapshot 2026-11-08.