The Tool Desk
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What is Android Bench 2.0?
Android Bench is Google’s benchmark for assessing AI models and coding agents on Android development work. The first version emphasized localized repository changes, such as a bug fix or a small feature request. Version 2.0 broadens the work to tasks Google describes as taking engineers days or weeks: creating apps, making substantial migrations, adding platform features, and converting cross-platform apps to native Android.
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The benchmark is intended to help developers compare AI tools on Android workflows and encourage progress in both models and agent harnesses. Its leaderboard measures specific model-agent combinations under Google’s setup; it does not establish how a tool will perform on every team’s codebase, constraints, or workflow.
What tasks are included?
The first published long-horizon set contains 30 tasks divided among four streams. Google’s methodology says scope can range from several files to hundreds, depending on the task.
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| Task stream | Tasks | Examples described by Google |
|---|---|---|
| App creation | 9 | A private, multi-screen food-delivery app built from visual mockups. |
| Migrations | 13 | Library and architecture migrations, including transformations such as Java to Kotlin or Retrofit to Ktor. |
| New features | 6 | Android platform features such as Picture-in-Picture and CameraX. |
| App conversions | 2 | Converting Flutter or React Native apps to native Android using Jetpack Compose. |
Google describes safeguards intended to test engineering reasoning rather than recall of an existing solution. Greenfield tasks use a private app codebase; some migrations target libraries or versions without an upstream migration to copy; and conversion tasks use apps without an existing native Android counterpart. Google also says it audits agent trajectories for reward hacking, hardcoded outputs, and external code lookups. The task dataset is private, while Google evaluates how to make it available without contaminating future evaluations.
How does the benchmark run and verify work?
According to Google’s 2026 methodology, tasks run in containerized virtual Android device environments. Harbor standardizes environment configuration, isolation, and metric collection. Each task receives five independent runs to account for nondeterministic model behavior.
Verification combines deterministic checks with UI-focused evaluation. Deterministic checks include Android instrumentation assertions, database inspection, system-boundary checks, and regression suites. For multimodal checks, scripted UI walkthroughs capture screens and inspect accessibility hierarchies. Google says Gemini 3.5 Flash acts as a visual judge, comparing results with reference images and examining those hierarchies.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsGoogle reports that calibration trials across 360 runs achieved 100% consistency across repeated runs, with Diff = 0.00. That is the methodology’s reported calibration result for this visual judge, not a general guarantee about visual evaluation systems.
What do pass rate and completion rate mean?
Pass rate measures complete task success: a run must earn a perfect score, pass all functional tests, meet full visual compliance, and avoid constraint violations. Completion rate is a continuous score from 0.0 to 1.0, intended to show partial progress even when a run does not fully pass.
Google’s completion calculation combines weighted functional, regression, requirements, and visual dimensions, then applies constraint multipliers. Task authors set the category weights, so a UI-heavy task can prioritize visual fidelity while architecture work can emphasize functionality and regression checks.
| Constraint outcome | Multiplier described in the methodology |
|---|---|
| Build failure | 0 |
| Cheating violation | 0 |
| Foreign-language files in a native Android task | 0 |
| Legacy API usage | 0.5 |
These measures answer different questions: pass rate is the share of runs that fully solve tasks, while completion rate captures graded progress on incomplete runs. Cost and latency also need context. Google cautions that early failures can reduce gross resource use without showing that a system is more efficient.
What has Google reported about model performance?
Google’s announcement says the models it evaluated generally performed better at writing new code than refactoring existing code. It identifies established transformations—such as Java-to-Kotlin conversion, replacing Retrofit with Ktor, and adding a ViewModel layer—as relative strengths. Runtime validation, breaking framework changes, unreleased libraries, and cross-platform app conversion remained difficult in the reported evaluations. These are findings for the models and tasks in this benchmark, not a universal ranking of coding-agent capability.
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The announcement also reported a highest long-horizon pass rate of around 28% at the time, compared with about 91% on the original benchmark tasks. The figures refer to different task sets and announcement-era results. They should not be read as a current leaderboard ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What did the leaderboard show on October 9, 2026?
The live leaderboard accessed on October 9, 2026 showed the following model-agent combinations. These are date-specific snapshots; leaderboard values can change as Google evaluates or updates entries.
| Model and agent | Pass rate | Average completion rate |
|---|---|---|
| Claude Opus 5.5 with Claude Code | 32.7% | 84.7% |
| GPT 6 Astra with Codex | 28.0% | 82.2% |
Google’s leaderboard also reports confidence intervals, average latency, average cost, and per-task results. The two headline rates alone do not show uncertainty, resource use, or where either combination struggled. For a meaningful comparison, look at the model-agent pairing, both scores, the confidence interval, latency, cost, task stream, evaluation constraints, and task-level failure patterns.
What are the benchmark’s limits?
- Virtual devices are not physical-device coverage. The methodology runs tasks on virtual Android devices; hardware-dependent functionality may rely on software mocks.
- Conversion walkthroughs depend on navigation. Deterministic UI walkthroughs can fail to reach later screens if an early navigation control does not render.
- Network conditions are simplified. Tasks use local mock servers, so the benchmark does not measure behavior under intermittent connections, slow responses, or backend errors.
- Coverage is still bounded. Google says future coverage is intended to expand to foldables, large screens, and Android Auto; those areas should not be assumed to be represented by the current task set.
These constraints make Android Bench 2.0 evidence about performance on its published task set and test setup, not a universal measure of coding-agent quality or a prediction of success on a particular production app. The official methodology and live leaderboard are the relevant Google sources for how the benchmark works and how its results change over time.
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