llmboard.aiLeaderboard Center
Overall
Overall RankingOpen Models
Tools
Model DirectoryCompare Models
Capabilities
CodingReasoningMathKnowledgeInstruction Following
Price & Efficiency
Price & ValueCapability vs. PriceRuntime Performance
Modalities
Image GenerationVideo GenerationSpeech ModelsEmbeddings
Core Benchmarks
GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-ProView all benchmarks
Methods
Scoring & Data
393 models668 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll BenchmarksReasoningMath

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai

multimodal benchmark

ScreenSpot

ScreenSpot is the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. The dataset comprises over 1,200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (text and icon/widget), designed to evaluate visual GUI agents' ability to accurately locate screen elements based on natural language instructions.

Updated Aug 11, 2026

Models16
Model coverage16
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

ScreenSpot Ranking

Higher score ranks better on this benchmark.

16 rows
Columns

Show columns

01ACQwen3 VL 32B InstructAlibaba Cloud / Qwen Team95.8%100.0%16CAug 11, 2026
02ACQwen3 VL 32B ThinkingAlibaba Cloud / Qwen Team95.7%93.3%16CAug 11, 2026
03ACQwen3 VL 235B A22B InstructAlibaba Cloud / Qwen Team95.4%86.7%16CAug 11, 2026
04ACQwen3 VL 235B A22B ThinkingAlibaba Cloud / Qwen Team95.4%80.0%16CAug 11, 2026
05ACQwen3 VL 30B A3B InstructAlibaba Cloud / Qwen Team94.7%73.3%16CAug 11, 2026
06ACQwen3 VL 30B A3B ThinkingAlibaba Cloud / Qwen Team94.7%66.7%16CAug 11, 2026
07ACQwen3 VL 8B InstructAlibaba Cloud / Qwen Team94.4%60.0%16CAug 11, 2026
08ACQwen3 VL 4B InstructAlibaba Cloud / Qwen Team94.0%53.3%16CAug 11, 2026
09ACQwen3 VL 8B ThinkingAlibaba Cloud / Qwen Team93.6%46.7%16CAug 11, 2026
10ACQwen3 VL 4B ThinkingAlibaba Cloud / Qwen Team92.9%40.0%16CAug 11, 2026
11ACQwen2.5 VL 32B InstructAlibaba Cloud / Qwen Team88.5%33.3%16CAug 11, 2026
12AMNova 2 ProAmazon88.1%26.7%16CAug 11, 2026
13ACQwen2.5 VL 72B InstructAlibaba Cloud / Qwen Team87.1%20.0%16CAug 11, 2026
14AMNova 2 OmniAmazon85.4%13.3%16CAug 11, 2026
15ACQwen2.5 VL 7B InstructAlibaba Cloud / Qwen Team84.7%6.7%16CAug 11, 2026
16AMNova 2 LiteAmazon83.3%0.0%16CAug 11, 2026

ScreenSpot Score Distribution

A closer view of the leading scores on this benchmark.

ScreenSpot

ScreenSpot Highlights

The leading models and scores on this benchmark.

Rank #1Qwen3 VL 32B Instruct95.8%Rank #2Qwen3 VL 32B Thinking95.7%Rank #3Qwen3 VL 235B A22B Instruct95.4%Rank #4Qwen3 VL 235B A22B Thinking95.4%

What is ScreenSpot?

What ScreenSpot measures and how its scores work.

ScreenSpot is the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. The dataset comprises over 1,200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (text and icon/widget), designed to evaluate visual GUI agents' ability to accurately locate screen elements based on natural language instructions.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
ScreenSpot
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
screenspot|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about ScreenSpot.

Which model scores highest on ScreenSpot?

Qwen3 VL 32B Instruct is currently ranked first with 95.8%.

What does ScreenSpot measure?

ScreenSpot is the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. The dataset comprises over 1,200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (text and icon/widget), designed to evaluate visual GUI agents' ability to accurately locate screen elements based on natural language instructions.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

16 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.