PlanBench-V - Policy Association: leaderboard

PlanBench-V (2026) tests vision-language models on interpreting Chinese territorial spatial planning maps with questions curated by professional planners; a GPT-4o-mini judge scores each answer from 0 to 2 against reference answers with annotated critical points. This board is the mean score on linking map evidence to the relevant planning policies and regulations; higher is better.

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 February 2027. 17 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.6 Plus1.62#222
2GPT-4o1.53#741
3Claude Opus 4.71.49#79
4Gemini 2.5 Pro1.47#266
5GPT-5.41.44#123
6Kimi K2.61.37#178
7Qwen 3.6 Flash1.35#424
8InternVL3-14B1.1#1182
9Qwen 2.5 VL 7B Instruct1.07#1833
10Qwen 2 VL 7B0.98#2261
11GPT-4o Mini0.96#1623
12InternVL3-8B0.93#1688
13Qwen 2 VL 2B0.93#6284

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

Interactive version: theaggregate.ai/benchmark?slug=planbench-v-policy-association · How It Works · Data refreshed daily, snapshot 2026-11-13.