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Android Bench 2.0 expands Google’s Android coding benchmark from localized repository changes to complex, long-horizon engineering tasks, and adds evaluation across coding-agent setups, visual UI checks, and a continuous completion score. Its results are useful for comparing specific model-agent combinations on Google’s task set—not for predicting how an agent will perform on every team’s apps or workflow.
What is Android Bench 2.0?
Android Bench 2.0 is Google’s benchmark for evaluating AI models and coding agents on Android engineering work. The first version focused on smaller, localized repository changes, such as bug fixes and feature requests. Version 2.0 adds tasks intended to represent work that can take an engineer days or weeks, including creating apps, migrating libraries or architecture, adding platform features, and converting cross-platform apps to native Android.
Google describes the update as a way to compare AI tools for Android workflows and encourage improvements to both models and agent harnesses—the systems that give a model tools and coordinate its work. The benchmark evaluates combinations of models and agents, so a leaderboard result belongs to that pairing under Google’s evaluation setup, not to a model in isolation.
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The first published long-horizon task set contains 30 tasks in four engineering streams. Google’s methodology gives task scopes ranging from several files to hundreds, depending on the work involved.
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| Task stream | Number of tasks | Examples |
|---|---|---|
| App creation | 9 | Building a private, multi-screen food-delivery app from visual mockups. |
| Migrations | 13 | Library and architecture migrations, including established transformations such as Java-to-Kotlin or Retrofit-to-Ktor. |
| New features | 6 | Adding Android platform capabilities such as Picture-in-Picture or CameraX. |
| App conversions | 2 | Rebuilding Flutter or React Native apps as native Android apps using Jetpack Compose. |
The tasks are designed to make copying an existing solution harder. Google says its greenfield work uses 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 audits agent trajectories for reward hacking, hardcoded outputs, and external code lookups. The task dataset is private while Google evaluates how it could be released without contaminating future evaluations.
How does Android Bench 2.0 evaluate an agent?
Isolated Android environments and repeated runs
According to Google’s methodology, tasks run in containerized virtual Android device environments. Harbor standardizes environment configuration, isolation, and metric collection. Each task is run five independent times to account for nondeterministic model behavior; the reported metrics therefore summarize runs rather than a single attempt.
Functional and visual checks
Deterministic checks include Android instrumentation assertions, database inspection, system-boundary checks, and regression suites. Multimodal verification adds scripted UI walkthroughs, screen captures, and accessibility-hierarchy checks. For visual verification, Google says Gemini 3.5 Flash judges results against reference images and inspects accessibility hierarchies.
Google reports that calibration trials across 360 runs produced 100% consistency across repeated runs for this visual judge (Diff = 0.00). That is a reported result for this calibration setup; it should not be read as a general guarantee that visual evaluation systems are always consistent.
What is the difference between pass rate and completion rate?
The two metrics answer different questions. Pass rate measures how often a run fully solves a task. Completion rate gives a continuous score for partial progress, including runs that fall short of a pass.
| Metric | What it measures | How to interpret it |
|---|---|---|
| Pass rate | The proportion of runs that meet the benchmark’s full-pass conditions. | Use it to compare complete task success. A pass requires a perfect score, all functional tests passing, full visual compliance, and no constraint violations. |
| Completion rate | A continuous score from 0.0 to 1.0 combining weighted functional, regression, requirements, and visual dimensions, followed by constraint multipliers. | Use it to compare measured partial progress as well as full success. Task authors set category weights, so the balance can differ: a UI task may emphasize visual fidelity, while architecture work may emphasize functionality and regression checks. |
Constraints can sharply affect completion scores. Google’s methodology assigns a zero multiplier for build failures, cheating violations, and foreign-language files in native Android tasks, and a 0.5 multiplier for legacy API usage. A strong completion rate is therefore not interchangeable with a high pass rate: the former can reflect substantial but incomplete work, while the latter counts only runs that satisfy all full-pass conditions.
What do the reported results show?
Google’s announcement says evaluated models generally did 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 evaluation. These findings describe the models and tasks Google tested, not every coding agent or Android project.
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The leaderboard accessed on October 9, 2026, showed the following model-agent pairings:
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| 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% |
These are dated leaderboard snapshots, not permanent rankings. Google’s leaderboard also reports confidence intervals, average latency, average cost, and per-task results. Those details help explain whether a difference is meaningful and where it comes from. Cost and latency need context: an agent that fails early can use fewer resources, which does not by itself show greater efficiency.
Google’s original announcement reported a highest long-horizon pass rate of about 28% at the time of publication, compared with about 91% on the original benchmark tasks. The later leaderboard accessed on October 9, 2026, showed a different leading entry. The announcement figure and later leaderboard value refer to different snapshots and should not be treated as a single current ranking.
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Android Bench 2.0 measures work in a controlled setup, and some conditions narrow what its results can establish:
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- Hardware-dependent behavior: Tasks run on virtual devices, and functionality that depends on physical hardware can rely on software mocks.
- UI walkthrough dependencies: App-conversion tests use deterministic walkthroughs. If an early navigation control fails to render, the driver may not reach later screens, so downstream behavior is not necessarily tested.
- Network conditions: Tasks use local mock servers. They do not measure behavior under intermittent connectivity, slow responses, or backend errors.
- Platform coverage: Google says future coverage is intended to expand to foldables, large screens, and Android Auto; the first task set should not be assumed to represent those areas comprehensively.
- Task-set generalization: A score measures performance on this task set and evaluation setup. It does not establish how an agent will handle a different codebase, team workflow, or project constraint.
How should you compare leaderboard entries?
Start with the model-agent pairing and the task stream, then read the metrics together rather than reducing performance to one number:
- Check pass rate for the share of runs that fully met the benchmark’s requirements.
- Check completion rate for measured partial progress, especially when full passes are uncommon.
- Read the confidence interval before treating a small score difference as a dependable lead.
- Compare latency and cost alongside outcomes, keeping early failures in mind when interpreting low resource use.
- Inspect per-task results and constraints to see whether a combination is strong on the work you care about and where it fails.
Matthew McCullough, Google’s VP of Product Management for Android Developer and author of the announcement, described the release this way: “Today we’re releasing the first set of long-horizon tasks (LHT), which are tasks of great complexity that take an engineer multiple days or even a week to complete.” The benchmark’s central change is the move toward evaluating that broader scope of Android work, while its scores remain evidence about performance under a defined set of tasks and conditions.
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