Benchmarks · Agents, tools & computer use
AndroidWorld
also: Android World
Can an agent operate a real Android phone — navigating apps, typing, tapping, adjusting settings — to complete a task described in natural language, with success checked against the device's actual resulting state?
Google Research / Google DeepMindReleased 23 May 2024
AndroidWorld, released by Google researchers in May 2024, is the mobile counterpart to desktop and browser benchmarks such as OSWorld: 116 tasks built around 20 real Android apps, each dynamically parameterised so the same underlying task can be phrased and instantiated in effectively endless variations rather than tested once and memorised. Success is checked against the phone’s real resulting state — a calendar entry that actually exists, a setting that actually changed — using dedicated verification logic built into each task rather than trusting the agent’s own report.
Early results tracked the pattern seen across this generation of agent benchmarks: the best agent at launch completed under a third of tasks against an 80% human baseline, and mobile GUI control remained a research problem through most of 2024. That changed as labs built dedicated screen-control models — Google DeepMind’s Gemini 2.5 Computer Use claimed to lead on AndroidWorld among other agent benchmarks by October 2025, without publishing a directly comparable figure — and the benchmark’s open, community-submitted leaderboard has since filled with specialised mobile-agent systems reporting scores above the original human baseline.
That openness is also the benchmark’s current weak point: while Google’s own tested models and a handful of named commercial products anchor the leaderboard’s middle, the highest-ranked entries increasingly come from less-documented teams whose methodology cannot be checked the way an arXiv paper’s can. As with several other benchmarks in this catalogue, AndroidWorld’s trajectory — rapid climb toward and past a human baseline, followed by growing reliance on self-reported leaderboard submissions — has become a familiar shape.
The set
116 hand-built tasks across 20 real, widely used Android apps, dynamically parameterised so each task can be instantiated in effectively unlimited natural-language variations rather than a fixed script. Each task ships with its own initialisation, success-checking and tear-down logic that inspects the device's real state afterwards, rather than grading the agent's own account of what it did.
Example
Create a new contact for {name}. Their number is {number}.google-research.github.io
Where it stands
Top entries on the open, community-submitted leaderboard have climbed above the roughly 80% human baseline reported at launch, though the highest-ranked submissions come from lightly documented teams and are harder to independently verify than the results Google itself has published for its own models.
How the top score changed hands
- May 2024Best agent at release30.6%Against an 80% human baseline on the same tasks, from the original paper.
- October 2025Gemini 2.5 Computer Usereported as leading (figure not published)Google DeepMind said the model led rival systems on AndroidWorld, WebVoyager and Online-Mind2Web, without publishing independently verified comparative figures.
Current best: AGI-0 — 97.4% Highest score on the official, community-submitted leaderboard as of research; several later entries tied it. Independent verification of community submissions is limited.