Android Control Low_EM

Android control benchmark evaluating autonomous agents on mobile device interaction tasks with low exact match scoring criteria

Qwen2.5 VL 72B Instruct from Alibaba Cloud / Qwen Team currently leads the Android Control Low_EM leaderboard with a score of 0.937 across 3 evaluated AI models.

Alibaba Cloud / Qwen TeamQwen2.5 VL 72B Instruct leads with 93.7%, followed by Alibaba Cloud / Qwen TeamQwen2.5 VL 32B Instruct at 93.3% and Alibaba Cloud / Qwen TeamQwen2.5 VL 7B Instruct at 91.4%.

Progress Over Time

Interactive timeline showing model performance evolution on Android Control Low_EM

State-of-the-art frontier
Open
Proprietary

Android Control Low_EM Leaderboard

3 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
72B
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
34B
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
8B
Notice missing or incorrect data?

FAQ

Common questions about Android Control Low_EM.

What is the Android Control Low_EM benchmark?

Android control benchmark evaluating autonomous agents on mobile device interaction tasks with low exact match scoring criteria

What is the Android Control Low_EM leaderboard?

The Android Control Low_EM leaderboard ranks 3 AI models based on their performance on this benchmark. Currently, Qwen2.5 VL 72B Instruct by Alibaba Cloud / Qwen Team leads with a score of 0.937. The average score across all models is 0.928.

What is the highest Android Control Low_EM score?

The highest Android Control Low_EM score is 0.937, achieved by Qwen2.5 VL 72B Instruct from Alibaba Cloud / Qwen Team.

How many models are evaluated on Android Control Low_EM?

3 models have been evaluated on the Android Control Low_EM benchmark, with 0 verified results and 3 self-reported results.

What categories does Android Control Low_EM cover?

Android Control Low_EM is categorized under multimodal and reasoning. The benchmark evaluates multimodal models.

More evaluations to explore

Related benchmarks in the same category

View all multimodal
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

reasoning
213 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

reasoning
119 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
107 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

reasoning
99 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
89 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

reasoningmultimodal
74 models