PlanBench-V - Decision Making: 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 choosing between planning alternatives under stated constraints; higher is better.

Metric: Mean score (out of 2) on choosing between planning alternatives under stated constraints, implementation 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 Plus1.71#222
2Gemini 2.5 Pro1.53#266
3GPT-5.41.51#123
4Qwen 3.6 Flash1.44#424
5GPT-4o1.43#741
6Claude Opus 4.71.32#79
7Kimi K2.61.32#178
8GPT-4o Mini1.18#1623
9InternVL3-8B1.07#1688
10Qwen 2.5 VL 7B Instruct1.05#1833
11InternVL3-14B0.92#1182
12Qwen 2 VL 2B0.79#6284
13Qwen 2 VL 7B0.66#2261

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-decision-making · How It Works · Data refreshed daily, snapshot 2026-11-13.