# ScreenSpot-Pro ![Category](https://img.shields.io/badge/Category-Reasoning-1f6feb) ![Metric](https://img.shields.io/badge/Metric-%25%20Accuracy-2da44e) ![Saturation](https://img.shields.io/badge/Saturation-Low-d29922) ![Created](https://img.shields.io/badge/Created-Apr%202025-555) [![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b)](https://arxiv.org/abs/2504.07981) [![Leaderboard](https://img.shields.io/badge/Leaderboard-gui--agent.github.io-0969da)](https://gui-agent.github.io/grounding-leaderboard) > **Quick answer:** ScreenSpot-Pro tests multimodal models' ability to ground GUI instructions (click the right element) on authentic high-resolution screenshots from 23 professional applications across 5 industries and 3 operating systems. It's substantially harder than earlier GUI-grounding benchmarks — the best model in the original paper reached only 18.9% accuracy. ## What ScreenSpot-Pro Measures ScreenSpot-Pro evaluates the GUI grounding capability of multimodal models: given a natural-language instruction and a screenshot, can the model correctly localize the target UI element? Unlike general-purpose GUI benchmarks built on consumer web/mobile apps, ScreenSpot-Pro uses authentic high-resolution screenshots from specialized professional software — spanning CAD, creative, scientific, and other domains — where small target sizes, dense layouts, and unfamiliar interfaces make grounding meaningfully harder. The paper found existing GUI grounding models perform poorly on this dataset (best baseline: 18.9%), and introduced ScreenSeekeR, a visual search method that narrows the search area using a planner's GUI knowledge, achieving a new state of the art of 48.1% without additional training — evidence that professional, high-resolution GUI grounding remains an open problem even for strong general-purpose models. ## Benchmark Specifications | Field | Value | |---|---| | Task category | Reasoning (multimodal GUI grounding) | | Metric | % accuracy (correct click-region localization) | | Coverage | 23 applications across 5 industries and 3 operating systems | | Saturation | Low | | Created by | Li et al. | | Source paper | [Li et al. 2025](https://arxiv.org/abs/2504.07981) | | Leaderboard | [gui-agent.github.io/grounding-leaderboard](https://gui-agent.github.io/grounding-leaderboard) | ## How ScreenSpot-Pro Is Scored Models are scored on the percentage of instructions where the predicted click location falls within the correct UI element's bounding region, evaluated across authentic high-resolution screenshots. Because targets in professional software are often small and visually dense, scores in the 40-75% range represent strong performance for current frontier multimodal models, well below saturation. ## ScreenSpot-Pro on Benchgen > No Benchgen results yet — [be the first to run ScreenSpot-Pro](/benchmarks/li-et-al/screenspot-pro). ## ScreenSpot-Pro vs Other Benchmarks | Benchmark | What it tests | Saturation | |---|---|---| | **ScreenSpot-Pro** | **High-resolution professional GUI element grounding** | **Low** | | [OSWorld-Verified](/benchmarks/osworld/osworld-verified) | End-to-end real desktop/web computer-use tasks | Low | | [OmniDocBench](/benchmarks/opendatalab/omnidocbench) | Document parsing (PDFs, scanned text) | Low | ScreenSpot-Pro isolates the grounding step (where to click) rather than full task completion, complementing end-to-end computer-use benchmarks like OSWorld-Verified. ## Frequently Asked Questions

What is ScreenSpot-Pro?

ScreenSpot-Pro is a benchmark testing multimodal models' ability to ground natural-language GUI instructions to the correct on-screen element, using authentic high-resolution screenshots from 23 professional applications.

What does a good ScreenSpot-Pro score look like?

The original paper's best baseline reached only 18.9% accuracy; a specialized visual-search method (ScreenSeekeR) reached 48.1% — scores above 40% represent strong performance on this still-unsaturated benchmark.

Who created ScreenSpot-Pro?

ScreenSpot-Pro was created by Kaixin Li, Ziyang Meng, and collaborators, published in April 2025.

Is ScreenSpot-Pro saturated?

No — even specialized methods reach under 50% accuracy, leaving substantial headroom for improvement in professional GUI grounding.

--- *Benchmark definition based on Li et al., "ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use" (arXiv:2504.07981). Last updated 2026-08-10.*

Rank	Model	Score
1	gpt-6-astra	92.7
2	muse-glimmer	75.4
3	lfm3-vl-2b	55.5

ScreenSpot-Pro — Results

ScreenSpot-Pro — Leaderboard

ScreenSpot-Pro Leaderboard