PlanBench-V - Caption: 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 describing a planning map; higher is better.

Metric: Mean score (out of 2) on map captioning, perception 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: Estimated already saturated. 17 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.6 Plus1.95#222
2GPT-5.41.9#123
3GPT-4o1.88#741
4Claude Opus 4.71.83#79
5InternVL3-8B1.78#1688
6Gemini 2.5 Pro1.78#266
7Qwen 3.6 Flash1.64#424
8Qwen 2.5 VL 7B Instruct1.63#1833
9InternVL3-14B1.58#1182
10Kimi K2.61.52#178
11Qwen 2 VL 7B1.39#2261
12Qwen 2 VL 2B0.93#6284
13GPT-4o Mini0.89#1623

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