PointGrounding
PointArena is a comprehensive platform for evaluating multimodal pointing across diverse reasoning scenarios. It includes Point-Bench, a curated dataset of ~1,000 pointing tasks across five categories: Spatial (positional references), Affordance (functional part identification), Counting (attribute-based grouping), Steerable (relative pointing), and Reasoning (open-ended visual inference). The benchmark evaluates language-guided pointing capabilities in vision-language models.
Qwen2.5-Omni-7B from Alibaba Cloud / Qwen Team currently leads the PointGrounding leaderboard with a score of 0.665 across 1 evaluated AI models.
What PointGrounding measures
PointGrounding is a multimodal benchmark that evaluates large language models on multimodal, spatial reasoning, grounding, and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.7, with the leader reaching 0.7.
Compare leaders on the best AI for multimodal, best AI for spatial reasoning, best AI for grounding and best AI for vision leaderboards.
Publication
- Paper
- PointArena: Probing Multimodal Grounding Through Language-Guided Pointing
- Authors
- Long Cheng, Jiafei Duan, Yi Ru Wang, Haoquan Fang, and 11 others
- Published
- arXiv
- 2505.09990
Abstract
Pointing serves as a fundamental and intuitive mechanism for grounding language within visual contexts, with applications spanning robotics, assistive technologies, and interactive AI systems. While recent multimodal models have started to support pointing capabilities, existing benchmarks typically focus only on referential object localization tasks. We introduce PointArena, a comprehensive platform for evaluating multimodal pointing across diverse reasoning scenarios. PointArena comprises three components: (1) Point-Bench, a curated dataset containing approximately 1,000 pointing tasks across five reasoning categories; (2) Point-Battle, an interactive, web-based arena facilitating blind, pairwise model comparisons, which has already gathered over 4,500 anonymized votes; and (3) Point-Act, a real-world robotic manipulation system allowing users to directly evaluate multimodal model pointing capabilities in practical settings. We conducted extensive evaluations of both state-of-the-art open-source and proprietary multimodal models. Results indicate that Molmo-72B consistently outperforms other models, though proprietary models increasingly demonstrate comparable performance. Additionally, we find that supervised training specifically targeting pointing tasks significantly enhances model performance. Across our multi-stage evaluation pipeline, we also observe strong correlations, underscoring the critical role of precise pointing capabilities in enabling multimodal models to effectively bridge abstract reasoning with concrete, real-world actions. Project page: https://pointarena.github.io/
Qwen2.5-Omni-7B leads with 66.5%.
Progress Over Time
Interactive timeline showing model performance evolution on PointGrounding
PointGrounding Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 7B | — | — |
FAQ
Common questions about PointGrounding.
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