Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Multi-modal models have achieved remarkable progress in recent years.
Nevertheless, they continue to exhibit notable limitations in spatial
understanding and reasoning, which are fundamental capabilities to achieving
artificial general intelligence. With the recent release of GPT-5, allegedly
the most powerful AI model to date, it is timely to examine where the leading
models stand on the path toward spatial intelligence. First, we propose a
comprehensive taxonomy of spatial tasks that unifies existing benchmarks and
discuss the challenges in ensuring fair evaluation. We then evaluate
state-of-the-art proprietary and open-source models on eight key benchmarks, at
a cost exceeding one billion total tokens. Our empirical study reveals that (1)
GPT-5 demonstrates unprecedented strength in spatial intelligence, yet (2)
still falls short of human performance across a broad spectrum of tasks.
Moreover, we (3) identify the more challenging spatial intelligence problems
for multi-modal models, and (4) proprietary models do not exhibit a decisive
advantage when facing the most difficult problems. In addition, we conduct a
qualitative evaluation across a diverse set of scenarios that are intuitive for
humans yet fail even the most advanced multi-modal models.
68a40b0db65388761d074558
Zhongang Cai
claimed_verified
2025-08-19T08:04:14.005Z
652d06833b5997ed71ce5c46
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/xZTXEcnEogEmBm_ledJQr.jpeg
Zhongang Cai
user
caizhongang
68a40b0db65388761d074559
Yubo Wang
68a40b0db65388761d07455a
Qingping Sun
68a40b0db65388761d07455b
Ruisi Wang
68a40b0db65388761d07455c
Chenyang Gu
68a40b0db65388761d07455d
Wanqi Yin
68a40b0db65388761d07455e
Zhiqian Lin
68a40b0db65388761d07455f
Zhitao Yang
68a40b0db65388761d074560
Chen Wei
68a40b0db65388761d074561
Xuanke Shi
68a40b0db65388761d074562
Kewang Deng
68a40b0db65388761d074563
Xiaoyang Han
68a40b0db65388761d074564
Zukai Chen
68a40b0db65388761d074565
Jiaqi Li
68a40b0db65388761d074566
Xiangyu Fan
68a40b0db65388761d074567
Hanming Deng
68a40b0db65388761d074568
Lewei Lu
68a40b0db65388761d074569
Bo Li
68a40b0db65388761d07456a
Ziwei Liu
68a40b0db65388761d07456b
Quan Wang
68a40b0db65388761d07456c
Dahua Lin
68a40b0db65388761d07456d
Lei Yang
claimed_verified
2025-08-19T08:04:11.955Z
6626a471430a124253f197c8
/avatars/f5747fdbe495d1296fed9d16d8c95857.svg
yl-1993
user
yl-1993
Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Multi-modal models have achieved remarkable progress in recent years.
Nevertheless, they continue to exhibit notable limitations in spatial
understanding and reasoning, which are fundamental capabilities to achieving
artificial general intelligence. With the recent release of GPT-5, allegedly
the most powerful AI model to date, it is timely to examine where the leading
models stand on the path toward spatial intelligence. First, we propose a
comprehensive taxonomy of spatial tasks that unifies existing benchmarks and
discuss the challenges in ensuring fair evaluation. We then evaluate
state-of-the-art proprietary and open-source models on eight key benchmarks, at
a cost exceeding one billion total tokens. Our empirical study reveals that (1)
GPT-5 demonstrates unprecedented strength in spatial intelligence, yet (2)
still falls short of human performance across a broad spectrum of tasks.
Moreover, we (3) identify the more challenging spatial intelligence problems
for multi-modal models, and (4) proprietary models do not exhibit a decisive
advantage when facing the most difficult problems. In addition, we conduct a
qualitative evaluation across a diverse set of scenarios that are intuitive for
humans yet fail even the most advanced multi-modal models.
Recent multi-modal models, including GPT-5, show significant progress in spatial intelligence but still lag behind human performance across various benchmarks.
multi-modal models spatial understanding reasoning artificial general intelligence GPT-5 spatial tasks benchmarks spatial intelligence qualitative evaluation