PlanBench-V - Policy Association: leaderboard
Metric: Mean score (out of 2) on linking map evidence to planning policies and regulations, association level; answers to expert-curated questions on Chinese territorial spatial planning maps are scored 0 to 2 by a GPT-4o-mini judge (temperature 0) against structured reference answers with annotated critical points; objective items use exact match or semantic similarity; higher is better. Source: arxiv.org. Saturation forecast: Around December 2026. 17 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Qwen 3.6 Plus | 1.62 |
| 2 | GPT-4o | 1.53 |
| 3 | Claude Opus 4.7 | 1.49 |
| 4 | Gemini 2.5 Pro | 1.47 |
| 5 | GPT-5.4 | 1.44 |
| 6 | Kimi K2.6 | 1.37 |
| 7 | Qwen 3.6 Flash | 1.35 |
| 8 | InternVL3-14B | 1.1 |
| 9 | Qwen 2.5 VL 7B Instruct | 1.07 |
| 10 | Qwen 2 VL 7B | 0.98 |
| 11 | GPT-4o Mini | 0.96 |
| 12 | InternVL3-8B | 0.93 |
| 13 | Qwen 2 VL 2B | 0.93 |
Interactive version: theaggregate.ai/benchmark?slug=planbench-v-policy-association · How It Works · Data refreshed daily, snapshot 2026-09-29.