Android Bench 2.0 measures Android software engineering by coding agents—not whether an agent can tap through a phone. It gives agents substantial repository tasks, then checks functional behavior, data, navigation, accessibility, visual fidelity and anti-cheating signals. AndroidWorld measures agents operating Android apps to reach a requested device state, while OSWorld measures open-ended computer use across desktop operating systems, websites, files and applications. Their scores answer different questions and should not be combined into a single ranking.
What does Android Bench 2.0 measure?
Android Bench 2.0 is an Android Developers benchmark for models and coding agents working inside real Android projects. Its central question is: can an agent plan, implement, refactor and validate a substantial Android change over a long sequence of engineering steps?
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The benchmark is intentionally broader than generating a small code snippet. Tasks require repository navigation, dependency and architecture decisions, compilation, test execution, emulator interaction and correction of failures. The official methodology divides its 30-task set into four streams:
App creation: nine tasks
Agents create a private, multi-screen Food Vibes application from design mocks. This stream tests whether an agent can turn requirements and visual references into a functioning app rather than merely modify an existing file.
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Migrations: 13 tasks
Migration tasks upgrade libraries or architectural components in existing projects. Examples include Retrofit to Ktor, RxJava to Coroutines, Hilt to Koin and Navigation 2 to Navigation 3. These changes expose the difficulty of preserving behavior while replacing APIs, wiring and project conventions.
New features: six tasks
Examples include Picture-in-Picture, Wear OS companion synchronization, home-screen widgets and CameraX. The agent must integrate platform capabilities into an existing application and demonstrate that the feature works at runtime.
App conversions: two tasks
These tasks convert Flutter or React Native applications to native Android using Jetpack Compose. They test cross-platform understanding, native UI implementation and behavioral parity.
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The task data are private. That limits outside inspection and exact reproduction, although private tasks also make it harder to memorize solutions. The methodology discusses trajectory audits, contamination and reward-hacking risks; those safeguards reduce but do not eliminate validity concerns.
Full implementation details are documented in the Android Bench 2.0 methodology.
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How are Android Bench tasks verified?
A run is not judged only by whether the project compiles. Android Bench combines several forms of verification so that an apparently complete patch must also behave correctly.
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- Instrumentation assertions: automated checks exercise required behavior.
- SQLite and Room inspection: database contents and persistence are checked directly.
- Outbound intents and events: integrations and emitted actions are inspected.
- Regression checks: existing behavior is tested after a migration or feature change.
- UI navigation: required screens and flows are traversed on the device.
- Accessibility-tree parsing: the evaluated interface is examined through accessibility semantics.
- Visual comparison: screenshots are compared with reference images and scored by Gemini 3.5 Flash as a visual judge.
- Anti-cheating patch inspection: submissions are checked for shortcuts that fake the expected result.
Pixel-perfect comparison alone can misclassify a semantically correct screen because system-level rendering varies. The visual judge is intended to assess the screenshot against the reference while allowing for ordinary pixel differences. Consequently, a passing implementation must satisfy both functional and presentation requirements, but the visual score should still be understood as a judgment under this specific method.
What do pass rate and completion rate mean?
Pass rate is an all-or-nothing result
Pass rate is the percentage of runs that achieve a perfect score and satisfy the complete set of checks. A run that implements most of a migration but misses one required behavior is not a pass.
Completion rate captures partial progress
Completion rate is a continuous value from 0.0 to 1.0 intended to represent how much of the task was completed, including runs that fail to pass. The methodology combines weighted scoring with penalties for violated constraints. It therefore distinguishes a nearly finished solution from one that made little useful progress.
Matthew McCullough, Android Developer product-management vice president, 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.”
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What does the Android Bench leaderboard report?
The leaderboard can show more than quality scores. Depending on the selected model and agent harness, it reports pass rate, completion rate, confidence intervals, average latency and average cost. These metrics answer different operational questions:
| Metric | What it tells you | What it does not tell you |
|---|---|---|
| Pass rate | How often a run satisfied every required check. | How close failed runs came to completion. |
| Completion rate | How much weighted task work was completed across runs. | That the resulting app is production-ready. |
| Confidence interval | Uncertainty around the reported estimate. | That two overlapping results are meaningfully different. |
| Average latency | Typical execution time under the listed setup. | Performance under another harness, model version or task limit. |
| Average cost | Reported run cost under the leaderboard configuration. | Your exact bill in a different environment or pricing period. |
Always record the model version, harness, task selection, run count and execution limits with a leaderboard result. Changing any of those can alter cost, latency and success.
How is Android Bench different from AndroidWorld?
AndroidWorld tests agents at the mobile-user layer rather than the repository-engineering layer. It provides a fully functioning Android environment and asks an agent to execute natural-language tasks by controlling apps and the device UI. Reward is derived from the resulting Android system state.
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| Question | Android Bench 2.0 | AndroidWorld |
|---|---|---|
| Primary capability | Android software engineering by coding agents. | Mobile UI control and task execution. |
| Agent interaction | Repository tools, shell commands, builds, tests and an Android virtual device. | Natural-language task execution through a functioning Android environment. |
| Task character | App creation, migrations, features and cross-platform conversion. | Parameterized tasks across installed Android apps. |
| Verification | Instrumentation, data inspection, intents/events, navigation, accessibility, visual judgment and patch checks. | Reward based on the Android system state after the task. |
| Data scope | 30 official long-horizon tasks; task data are private; five runs per task. | 114 tasks across 20 apps in the paper description; later releases can differ. |
A high AndroidWorld score can show that an agent reliably operates apps, but it does not show that the agent can migrate a codebase or diagnose a compiler failure. Conversely, Android Bench says little about open-ended tap, type and navigation behavior outside the evaluated repositories.
Read the paper description at Google Research’s AndroidWorld page, and identify the release whose task count you cite.
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Can you compare Android Bench with OSWorld?
OSWorld moves beyond Android into general computer use. Its tasks run in real computer environments spanning operating systems, web applications, desktop applications, files and workflows that cross application boundaries. The agent must ground actions in a graphical interface and complete an open-ended objective.
The OSWorld project page describes 369 tasks. Eight Google Drive tasks may require manual configuration or exclusion, producing 361 tasks in that setup. This qualification matters: the denominator depends on whether those tasks are configured and included.
| Benchmark | Platform and environment | Typical work | How to interpret success |
|---|---|---|---|
| Android Bench 2.0 | Dockerized Android projects with hardware-accelerated virtual devices. | Long-horizon coding, migrations, features and app conversion. | Ability to implement and validate substantial Android changes in the stated harness. |
| AndroidWorld | Fully functioning Android device environment with multiple apps. | Natural-language mobile tasks and UI manipulation. | Ability to bring the Android system to a requested state. |
| OSWorld | Real computer environments across operating systems, web and desktop apps, files and cross-app workflows. | GUI grounding and multi-step computer use. | Ability to complete desktop-oriented workflows, not Android repository implementation. |
OSWorld’s task count, like AndroidWorld’s, is release-sensitive. The benchmark’s project description is available at the OSWorld project page. Do not compare an Android Bench pass percentage with an OSWorld success percentage as if both measured the same event: the platforms, instructions, action spaces, environments and graders differ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the best benchmarks for AI agents?
There is no single best benchmark independent of the capability you need to select. Choose the benchmark whose environment resembles the work you want the agent to perform.
Choose Android Bench for Android repository work
- Use it to assess app creation from requirements or design references.
- Use it to test library and architecture migrations where old behavior must survive.
- Use it to evaluate new Android platform features and runtime validation.
- Use it to study conversion from Flutter or React Native to native Android and Compose.
Choose AndroidWorld for mobile interaction
- Use it when the agent must operate existing Android apps.
- Use it to test navigation, data entry, settings changes and cross-app mobile workflows.
- Use it when the desired outcome is a device state rather than a source-code patch.
Choose OSWorld for broad computer use
- Use it for browser and desktop application workflows.
- Use it to test file handling and actions that cross application boundaries.
- Use it when operating-system and GUI grounding are central requirements.
For a coding-agent procurement decision, Android Bench is the closest of these three to day-to-day Android engineering. For a mobile assistant, AndroidWorld is more representative. For a general desktop operator, OSWorld is the relevant axis. A serious evaluation can use more than one, but each score must remain labeled with its benchmark and conditions.
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Before placing two results in the same chart or paragraph, report the following axes:
- Platform: Android repository, Android device, desktop operating system or another environment.
- Task type: coding, migration, feature implementation, UI operation, file workflow or cross-application work.
- Environment: virtual device, Docker container, real computer, installed apps and network assumptions.
- Task-set scope: official count, release version, private or public data and any excluded tasks.
- Verification: test assertions, state-based reward, visual judgment, human review or a combination.
- Score definition: binary pass, continuous completion, reward or another measure.
- Repeat count: number of independent runs and whether the result includes confidence intervals.
- Operational metrics: latency and cost, including model, harness and execution limits.
Only after those details are aligned should you make a narrow comparison such as “agent A completed more Android Bench migrations than agent B under the same harness.” A statement such as “agent A is 20 percentage points better at computer use” is not justified when the percentages come from different benchmarks.
What does Android Bench reveal about current coding-agent weaknesses?
The 17 September 2026 Android Developers announcement reported that models generally wrote new code more successfully than they refactored existing code. Breaking framework changes, runtime validation and cross-platform-to-Android conversions were more difficult. That pattern is consistent with the task design: a migration must preserve hidden assumptions in an established codebase, while a new feature can sometimes be implemented along a cleaner path.
The result is useful for engineering planning. A team should not infer from success on a greenfield coding test that an agent can safely upgrade a production navigation stack, replace a dependency across modules or verify behavior on a device. Those are precisely the kinds of long-horizon, stateful changes Android Bench emphasizes.
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What Android Bench 2.0 does not prove
- It does not establish universal coding ability across languages, repositories or mobile platforms.
- It does not predict performance on tasks outside its 30-task mix.
- It does not show that a model’s code is maintainable, secure or ready for production without human review.
- It does not make its results directly comparable with AndroidWorld or OSWorld percentages.
- It does not remove uncertainty caused by model updates, harness choices, task limits, infrastructure and evaluator rules.
- Its private task data limit independent reproduction and detailed external audit.
These are properties of the measurement boundary, not flaws that make the benchmark useless. Android Bench is valuable precisely when the question is bounded to substantial Android engineering under its documented setup.
A practical reporting template
When publishing or reviewing an agent result, write it in a form another reader can interpret:
Benchmark: Android Bench 2.0
Task scope: 30 official long-horizon tasks (9 creation, 13 migrations, 6 features, 2 conversions)
Runs: five independent runs per task
Agent/model: [exact version]
Harness and limits: [exact configuration]
Pass rate: [value, with confidence interval if supplied]
Completion rate: [value]
Average latency and cost: [values and conditions]
Environment: fresh Docker containers, hardware-accelerated Android virtual devices
Evaluation: functional, data, navigation, accessibility, visual and patch checksFor AndroidWorld, name the paper or project release and its task count. For OSWorld, state whether the eight Google Drive tasks were configured and included. This prevents a version-specific denominator from being presented as a timeless fact.
Bottom line: three benchmarks, three capabilities
Android Bench 2.0 is the relevant measure for long-horizon Android coding and repository change. AndroidWorld measures whether an agent can operate Android apps and reach a target device state. OSWorld measures general computer use across desktop operating systems and applications. Their complementary results are more informative than a forced overall ranking, provided every score is reported with its task scope, environment, verification method and run conditions.
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