PlanBench-V - Element Recognition: 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 recognizing map layout, text and planning elements; higher is better.

Metric: Mean score (out of 2) on element recognition (map layout, textual and planning elements), 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: Around February 2027. 17 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.6 Plus1.75#222
2Kimi K2.61.52#178
3Gemini 2.5 Pro1.41#266
4Qwen 3.6 Flash1.32#424
5GPT-5.41.23#123
6Claude Opus 4.71.19#79
7Qwen 2.5 VL 7B Instruct1.1#1833
8GPT-4o1.05#741
9InternVL3-8B0.99#1688
10InternVL3-14B0.93#1182
11Qwen 2 VL 7B0.9#2261
12Qwen 2 VL 2B0.74#6284
13GPT-4o Mini0.66#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-element-recognition · How It Works · Data refreshed daily, snapshot 2026-11-13.