Google has formally launched Android Bench, a brand new leaderboard and analysis framework designed to measure how Massive Language Fashions (LLMs) carry out particularly on Android growth duties. The dataset, methodology, and take a look at harness have been made open-source and are publicly obtainable on GitHub.
Benchmark Methodology and Process Design
Basic coding benchmarks typically fail to seize the platform-specific dependencies and nuances of cellular growth. Android Bench addresses this by curating a process set sourced instantly from real-world, public GitHub Android repositories.
Evaluated situations cowl various issue ranges, together with:
- Resolving breaking modifications throughout Android releases.
- Area-specific duties, similar to networking on Put on OS units.
- Migrating code to the newest model of Jetpack Compose (Android’s fashionable toolkit for constructing native person interfaces).
To make sure a model-agnostic analysis, the framework prompts an LLM to repair a reported challenge after which verifies the repair utilizing normal developer testing practices:
- Unit exams: Assessments that confirm small, remoted blocks of code (like a single perform or class) while not having the Android framework.
- Instrumentation exams: Assessments that run on a bodily Android system or emulator to confirm how the code interacts with the precise Android system and APIs.
Mitigating Information Contamination
A major problem for builders evaluating public benchmarks is information contamination. This happens when an LLM is uncovered to the analysis duties throughout its coaching course of, ensuing within the mannequin memorizing the solutions slightly than demonstrating real reasoning and problem-solving capabilities.
To make sure the integrity of the Android Bench outcomes, Google workforce carried out a number of preventative measures:
- Guide overview of agent trajectories: Builders overview the step-by-step reasoning and motion paths the mannequin takes to reach at an answer, guaranteeing it’s actively fixing the issue.
- Canary string integration: A singular, identifiable string of textual content is embedded into the benchmark dataset. This acts as a sign to internet crawlers and information scrapers utilized by AI corporations to explicitly exclude this information from future mannequin coaching runs.
Preliminary Android Bench Leaderboard Outcomes
For the preliminary launch, the benchmark strictly measures base mannequin efficiency, deliberately omitting complicated agentic workflows or instrument use.
The Rating represents the common proportion of 100 take a look at circumstances efficiently resolved throughout 10 unbiased runs for every mannequin. As a result of LLM outputs can differ between runs, the outcomes embrace a Confidence Interval (CI) with a p-value < 0.05. The CI supplies the anticipated efficiency vary, indicating the statistical reliability of the mannequin’s rating.
On this first launch, fashions efficiently accomplished between 16% and 72% of the duties.
| Mannequin | Rating (%) | CI Vary (%) | Date |
| Gemini 3.1 Professional Preview | 72.4 | 65.3 — 79.8 | 2026-03-04 |
| Claude Opus 4.6 | 66.6 | 58.9 — 73.9 | 2026-03-04 |
| GPT-5.2-Codex | 62.5 | 54.7 — 70.3 | 2026-03-04 |
| Claude Opus 4.5 | 61.9 | 53.9 — 69.6 | 2026-03-04 |
| Gemini 3 Professional Preview | 60.4 | 52.6 — 67.8 | 2026-03-04 |
| Claude Sonnet 4.6 | 58.4 | 51.1 — 66.6 | 2026-03-04 |
| Claude Sonnet 4.5 | 54.2 | 45.5 — 62.4 | 2026-03-04 |
| Gemini 3 Flash Preview | 42.0 | 36.3 — 47.9 | 2026-03-04 |
| Gemini 2.5 Flash | 16.1 | 10.9 — 21.9 | 2026-03-04 |
Be aware: You may strive all of the evaluated fashions in your personal Android tasks utilizing API keys within the newest steady model of Android Studio.
Key Takeaways
- Specialised Focus Over Basic Benchmarks: Android Bench addresses the shortcomings of generic coding benchmarks by particularly measuring how properly LLMs deal with the distinctive complexities, APIs, and dependencies of the Android ecosystem.
- Grounded in Actual-World Situations: As a substitute of remoted algorithmic exams, the benchmark evaluates fashions towards precise challenges pulled from public GitHub repositories. Duties embrace resolving breaking API modifications, migrating legacy UI code to Jetpack Compose, and dealing with device-specific networking (e.g., on Put on OS).
- Verifiable, Mannequin-Agnostic Testing: Code technology is evaluated based mostly on performance, not methodology. The framework mechanically verifies the LLM’s proposed fixes utilizing normal Android engineering practices: remoted unit exams and emulator-based instrumentation exams.
- Strict Anti-Contamination Measures: To make sure fashions are literally reasoning slightly than regurgitating memorized coaching information, the benchmark employs handbook evaluations of agent reasoning paths and makes use of ‘canary strings’ to forestall AI internet crawlers from ingesting the take a look at dataset.
- Baseline Efficiency Established: The first model of the leaderboard focuses purely on base mannequin efficiency with out exterior agentic instruments. Gemini 3.1 Professional Preview at present leads with a 72.4% success fee, highlighting a large variance in present LLM capabilities (which vary from 16.1% to 72.4% throughout examined fashions).
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