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Android Bench 2.0 measures AI agents’ ability to complete substantial Android software-engineering tasks inside real projects. It is not a test of tapping through a phone interface. Agents create apps, migrate libraries and architectures, add platform features, and convert cross-platform projects to native Android. Its pass rate counts completely successful runs, while its continuous completion rate credits partial progress.
That makes Android Bench complementary to, rather than interchangeable with, AndroidWorld and OSWorld. AndroidWorld evaluates agents that operate Android apps and change device state through a mobile UI. OSWorld evaluates open-ended computer use across desktop operating systems, web applications, files, and cross-application workflows. Their percentages answer different questions and should not be combined into one universal ranking.
What does Android Bench 2.0 measure?
Android Bench 2.0 is an Android Developers benchmark for coding agents working on realistic Android repositories. A task can require creating a multi-screen application, changing an existing architecture, adopting a new Android capability, or translating a Flutter or React Native app into native Android with Jetpack Compose. The agent must make code changes, build and run them, and produce behavior and screens that satisfy the benchmark’s checks.
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The official Android Bench 2.0 methodology describes 30 long-horizon tasks. The taskset is private, so outsiders cannot inspect every prompt, fixture, or reference implementation. Results therefore describe performance under Android Bench’s selected tasks, infrastructure, agent harnesses, and grading rules—not an unlimited measure of Android ability.
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Its central distinction is software engineering versus interface operation:
- Android Bench: an agent edits and validates an Android codebase.
- AndroidWorld: an agent controls functioning Android apps to reach a requested device state.
- OSWorld: an agent performs multi-step work with graphical interfaces, files, and applications on general-purpose computers.
The four Android Bench task streams
The 30 tasks are divided into four streams. This mixture intentionally tests both greenfield implementation and the harder work of changing an existing project without breaking it.
| Stream | Tasks | What the agent must do | Examples in the methodology |
|---|---|---|---|
| App creation | 9 | Build the private, multi-screen Food Vibes app from design mocks. | Implement screens, navigation, data behavior, and the visual details represented by the references. |
| Migrations | 13 | Upgrade libraries or application architecture in an existing project. | Retrofit to Ktor, RxJava to Coroutines, Hilt to Koin, and Navigation 2 to Navigation 3. |
| New features | 6 | Add Android or companion-device capabilities while preserving existing behavior. | Picture-in-Picture, Wear OS companion synchronization, home-screen widgets, and CameraX. |
| App conversions | 2 | Convert a Flutter or React Native application to native Android. | Recreate the app with Android technologies, including Jetpack Compose where required. |
These categories are important when reading a result. A model that performs well on new screens may still struggle with framework-breaking migrations or preserving behavior during a cross-platform conversion. A single aggregate number can conceal those differences, so a serious report should identify the stream and individual task coverage whenever that information is available.
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How Android Bench runs an agent
Each attempt starts in a fresh Docker container with a hardware-accelerated Android virtual device. Harbor supplies standardized environment configuration and metrics, while the benchmark supports multiple agent harnesses. Shell operations use structured tool calling rather than an informal terminal transcript. Every task receives five independent runs to account for non-deterministic model and tool behavior.
The five-run design changes how a percentage should be read. A run is the unit scored as a success or failure; repeating a task five times exposes agents that occasionally work rather than reliably completing it. A leaderboard result also depends on the model version, harness, execution limits, and other setup details. Those variables belong in any reproduction or comparison record.
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Android Bench’s private tasks are a deliberate defense against overfitting and contamination, but they also limit independent inspection and exact reproduction. The methodology discusses trajectory audits and other safeguards against reward hacking and contaminated solutions. Those measures reduce risks; they do not prove that every validity threat has been eliminated.
What Android Bench’s verification checks
Passing is more than compiling an APK or matching one screenshot. The methodology combines several kinds of evidence:
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- Checks of outbound intents and emitted events.
- Regression checks to detect broken existing functionality.
- UI navigation and accessibility-tree parsing.
- Visual comparison against reference screens.
- Patch inspection intended to catch benchmark-specific cheating.
Visual grading uses Gemini 3.5 Flash as a visual judge. Android Developers notes that ordinary pixel comparison can mark a semantically correct screen wrong because of system-level variation. The visual judge compares the submitted screenshots with reference images and returns a score, allowing visual evaluation to account for meaning rather than treating every rendering difference as an error.
Pass rate versus completion rate
Android Bench reports two quality measures because binary success alone can hide useful progress.
| Metric | Definition | How to use it |
|---|---|---|
| Pass rate | The percentage of runs that receive a perfect score and satisfy the complete set of checks. A partially working solution is not a pass. | Use it for reliability: how often the agent finishes the entire task to the benchmark’s standard. |
| Completion rate | A continuous value from 0.0 to 1.0 intended to capture how much of a task was completed, including runs that did not fully pass. The methodology combines weighted scoring with penalties for violated constraints. | Use it to distinguish a near-complete attempt from one that made little progress. |
Matthew McCullough, Android’s vice president of product management, explained the rationale in the 17 September 2026 announcement: “On multi-day engineering tasks, binary pass or fail grading doesn’t capture the full picture.” The two metrics should be read together. A high completion rate with a low pass rate indicates that agents often get close but fail one or more final requirements; a high pass rate indicates dependable end-to-end completion.
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The live Android Bench leaderboard also reports confidence intervals, average latency, and average cost. These are operational measures, not additional definitions of task quality. Latency and cost can change with model versions, harness settings, execution limits, and infrastructure, so quote them with the corresponding leaderboard configuration.
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In the Android Developers announcement dated 17 September 2026, the highest reported pass rate for the long-horizon taskset was around 28%, compared with around 91% on the original tasks. These figures are time-bound snapshots, not permanent benchmark constants; check the live leaderboard before publishing or relying on a current ranking.
The same announcement says agents generally write new code more successfully than they refactor existing code. Runtime validation, breaking framework changes, and conversions from cross-platform frameworks to Android were more difficult. That pattern is consistent with the task mix: migrations and conversions require the agent to understand prior design decisions, preserve behavior, and adapt to constraints that are not present in a blank project.
How is Android Bench different from AndroidWorld?
AndroidWorld is a dynamic environment for agents that execute natural-language instructions by controlling Android applications and the device UI. Its reward is derived from the resulting Android system state. The agent might need to navigate apps, enter information, change settings, or complete a cross-app workflow; it is not asked to modify a repository and submit a software patch.
The AndroidWorld paper describes 114 programmatic tasks across 20 apps, with parameterization providing effectively unlimited variations. The project site and current repository can describe later task counts, so a report must identify whether it is quoting the paper’s 2025 taskset or a later release. Mixing those versions makes a numerical comparison misleading.
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| Question | Android Bench 2.0 | AndroidWorld |
|---|---|---|
| Primary capability | Implementing and validating substantial Android code changes. | Operating mobile apps to reach a requested device state. |
| Agent interface | Repository files, shell tools, build systems, and an Android virtual device. | Natural-language tasks executed through a functioning Android environment and its UI. |
| Typical output | A modified project that passes behavioral and visual checks. | A device whose state satisfies the task’s programmatic reward. |
| What a score means | Engineering completion and reliability within the private Android taskset. | Mobile UI grounding, planning, and action execution for the selected apps and tasks. |
A high AndroidWorld score cannot establish that an agent can perform a library migration, and a high Android Bench score cannot establish that it can reliably navigate an unfamiliar phone interface. They exercise different layers of an Android product.
How is Android Bench different from OSWorld?
OSWorld evaluates open-ended computer use in real computer environments. Its tasks span operating systems, web and desktop applications, file input/output, and workflows that cross application boundaries. The benchmark is oriented toward graphical-interface grounding and multi-step computer use rather than Android repository implementation.
The OSWorld project page describes 369 computer tasks. Eight Google Drive tasks may require manual configuration or exclusion; in that setup, 361 tasks remain. Because setup choices affect the denominator, a result should state which task set was actually run.
| Dimension | Android Bench 2.0 | AndroidWorld | OSWorld |
|---|---|---|---|
| Platform | Android projects plus an accelerated Android virtual device. | A functioning Android environment with mobile apps. | Real computer environments across operating systems. |
| Task character | Long-horizon coding: creation, migrations, features, and conversions. | Parameterized natural-language mobile tasks. | Open-ended desktop, web, file, and cross-application workflows. |
| Task scope cited by the source | 30 official tasks in the 2.0 methodology, five runs each. | 114 programmatic tasks across 20 apps in the paper description; later releases may differ. | 369 tasks initially described; eight Drive tasks can require setup or exclusion. |
| Verification | Instrumentation, data and event checks, regression tests, accessibility, visual judgment, and patch inspection. | Reward derived from Android system state. | Task-specific evaluation in the stated computer environment. |
| Best interpretation | Can the agent engineer and validate a substantial Android change? | Can the agent operate mobile software to produce the requested state? | Can the agent ground actions and complete multi-step computer work across applications? |
Which benchmark should you use?
Choose based on the capability you need to measure, not on which leaderboard has the largest percentage.
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- You are selecting an agent to build features in an Android repository.
- You care about migrations, refactoring, regression safety, and integration with Android frameworks.
- You need both functional and visual acceptance criteria.
- You want a long-horizon test rather than a short code-generation sample.
Choose AndroidWorld for mobile interaction
- You are evaluating phone-use agents, accessibility automation, or mobile workflow planning.
- The agent’s output is a changed app or device state, not a source-code patch.
- You need parameterized tasks across multiple installed applications.
Choose OSWorld for general computer use
- You need to test browser, desktop application, file-management, or cross-application workflows.
- Your product targets general computer-use agents rather than Android developers.
- You want to measure visual grounding and action planning across operating systems.
Organizations building a broad agent evaluation can use all three, but should report them as separate capability axes. Do not average their pass rates or describe one as a proxy for another.
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How to compare reported results responsibly
Before comparing two leaderboard entries, record the following fields:
- Benchmark release: identify Android Bench 2.0, the AndroidWorld paper or a later project release, or the OSWorld setup being used.
- Task denominator: state the number of tasks actually eligible for scoring, including any exclusions.
- Task type: separate repository engineering, mobile UI operation, and desktop computer use.
- Environment: note the operating system, virtual device or real computer, installed applications, and hardware acceleration.
- Agent and harness: record model version, tool interface, execution limits, and orchestration harness.
- Score definition: distinguish Android Bench pass rate from completion rate and from another benchmark’s reward or success definition.
- Repeat protocol: include Android Bench’s five independent runs per task or the repetition policy used elsewhere.
- Operational metrics: if citing latency or cost, include the date, model, limits, and setup that produced them.
This reporting discipline prevents a common error: presenting a high score on a narrow, short, or UI-only task set as evidence of general software-engineering competence.
Important limitations
- Private data: Android Bench’s task data are not publicly inspectable, which limits exact reproduction and independent auditing.
- Scope dependence: every score reflects the selected tasks, infrastructure, model-agent pairing, and verification rules.
- Version sensitivity: AndroidWorld task counts and OSWorld setup details can change; identify the release instead of treating counts as timeless.
- Infrastructure effects: runtime, cost, emulator behavior, and system-level rendering can affect results.
- Security and validity risks: trajectory audits, private tasks, and patch checks mitigate reward hacking and contamination but cannot guarantee perfect benchmark validity.
These limitations do not make the benchmarks unusable. They define what each result can legitimately support: a bounded statement about an agent’s performance under specified conditions.
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A practical reading of an Android Bench score
Suppose two agents have similar completion rates but different pass rates. The agent with the higher pass rate finishes the entire acceptance suite more reliably; the other may make substantial partial progress but fail a final migration detail, runtime check, or visual requirement. If costs and latency are also reported, a team can decide whether occasional manual repair is acceptable or whether dependable end-to-end completion is essential.
Conversely, a lower completion rate on Android Bench does not prove that an agent is poor at mobile UI control or desktop automation. Those capabilities are outside the benchmark’s target. Use AndroidWorld or OSWorld when the product requirement concerns those interaction layers.
Sources and current figures
For methodology details, task composition, environment, scoring, verification, and safeguards, consult the Android Bench 2.0 methodology. For the dated announcement and its long-horizon result context, see Android Bench 2.0: Pushing the frontier with challenging long-horizon tasks. Use the live leaderboard for current scores rather than reusing the approximately 28% and 91% figures outside their 17 September 2026 context.
The Bottom Line
Android Bench 2.0 is the relevant benchmark when the question is whether an AI agent can engineer and validate a substantial Android project. AndroidWorld measures mobile UI operation, and OSWorld measures broad computer use. Treat their scores as separate capability measurements, always naming the release, task set, environment, verification method, and score definition.
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