ScreenSpot-v2 — Results
| Rank | Model | Score |
|---|---|---|
| 1 | lfm2-5-vl-3b | 80.7 |
ScreenSpot-v2
1 phaseActive
GUI element grounding benchmark spanning mobile, web, and desktop screens. Metric: % click-region accuracy, reported per-platform and averaged.
Overview
ScreenSpot-v2
Quick answer: ScreenSpot-v2 tests a multimodal model's ability to ground natural-language instructions to the correct on-screen element across mobile, web, and desktop GUI screenshots. It's a refined, re-annotated version of the original ScreenSpot benchmark introduced alongside SeeClick. LFM2.5-VL-3B averages 80.7% across platforms as of August 2026.
At a Glance
What it tests: Whether a vision-language model can locate and click the correct GUI element (button, icon, text field) given a natural-language instruction, across mobile, web, and desktop screenshots.
Why it matters: GUI grounding accuracy is a prerequisite for autonomous computer-use and screen-navigation agents — a model that can't reliably locate the right element can't reliably complete multi-step UI tasks.
Known limitations: As models have improved on the original ScreenSpot benchmark, scores have crept toward saturation; the harder, higher-resolution ScreenSpot-Pro benchmark was introduced separately to keep headroom for frontier models.
What ScreenSpot-v2 Measures
ScreenSpot-v2 is a refined and re-annotated version of the original ScreenSpot benchmark, which was introduced alongside the SeeClick GUI-grounding agent. It presents a model with a screenshot and a natural-language instruction (e.g., "click the settings icon") and scores whether the model's predicted click location falls within the correct on-screen element's bounding box. The benchmark is split across three platforms — Mobile, Web, and Desktop — each reported separately and then averaged.
Because it draws directly from GUI grounding — the core skill needed for computer-use and screen-navigation agents — ScreenSpot-v2 is widely cited by vision-language model labs (including Liquid AI, Alibaba's Qwen team, and Google's Gemma team) as a proxy for real-world agentic screen-understanding capability.
Benchmark Specifications
| Field | Value |
|---|---|
| Task category | Reasoning (GUI grounding) |
| Metric | % accuracy (click-region grounding), per-platform and averaged |
| Saturation | Medium |
| Created by | Cheng et al. (re-annotated from the original ScreenSpot / SeeClick benchmark) |
| Source paper | Cheng et al. 2024 (SeeClick) |
| GitHub | niuzaisheng/ScreenSpot |
| Dataset | likaixin/ScreenSpot on HuggingFace |
How ScreenSpot-v2 Is Scored
For each instruction, the model outputs a predicted click coordinate or bounding box; a prediction is scored correct if it falls within the ground-truth element's region. Scores are reported per platform (Mobile, Web, Desktop) and averaged into a single headline number.
State-of-the-Art Results
| Rank | Model | Score | Source | Date |
|---|---|---|---|---|
| 1 | LFM2.5-VL-3B | 80.7% (avg) | LFM2.5-VL-3B launch blog | 2026-08 |
Score sourced from Liquid AI's LFM2.5-VL-3B announcement, August 2026.
ScreenSpot-v2 on Benchgen
No Benchgen results yet — be the first to run ScreenSpot-v2.
ScreenSpot-v2 vs Other Benchmarks
| Benchmark | What it tests | Saturation |
|---|---|---|
| ScreenSpot-v2 | GUI element grounding across mobile/web/desktop | Medium |
| ScreenSpot-Pro | High-resolution GUI grounding in 23 professional apps | Low |
ScreenSpot-Pro was introduced as a deliberately harder, higher-resolution successor once ScreenSpot-v2 scores began climbing toward saturation for frontier models.
Run ScreenSpot-v2 on Your Model
Benchgen lets you evaluate your own vision-language model on GUI grounding tasks and track screen-understanding accuracy across model versions.