Google has pushed Android Bench into version 2.0, shifting its assessment model toward demanding, long-horizon coding tasks that take engineers days or even weeks. Where the first version focused mostly on minor tweaks—such as bug fixes or small feature additions—to existing projects, the updated suite now rates how well AI can build apps from scratch, port cross-platform software to Android, or add substantial new capabilities. These are complexity levels far above simple repository maintenance.
Beyond Pass or Fail: Grading with Nuance
With these higher stakes, the evaluation methodology has gotten a full upgrade. Android Bench 2.0 abandons the binary pass/fail model in favor of a continuous scoring system. It measures outcomes along multiple axes: how fully the feature works, whether it mirrors the intended visual design, and if it avoids breaking existing functionality. Penalties come into play for diverging from evaluation instructions or violating structural requirements.
How the Leading Models Stack Up
Several front-line models have been assessed under the new benchmark. Gemini 3.7/3.8 Flash, OpenAI’s GPT-6, Anthropic’s Fable 5.1, Kimi K3, and Qwen 3.8 Max are among those tested. Yet none reach perfection—on cross-platform app porting, the strongest performers top out at about an 80 % completion rate, and no model captures full marks across all long-horizon tasks.
Interestingly, generative agents tied to specific providers have been evaluated too—examples include Gemini 3.8 Flash paired with Google’s Antigravity, or GPT-5.6 Sol using Codex. The results suggest that how an agent is implemented has a big effect on the outcome.
Where Models Are Strong — and Where They Struggle
Models handle feature creation and app development from scratch significantly better than refactoring or migrating existing codebases. The data shows that while tasks like converting Java to Kotlin or swapping Retrofit for Ktor are handled reasonably well, breaking into more opaque territory—runtime errors, missing dependencies, or using unreleased libraries—trip them up significantly.
Even the most capable models don’t yet hit high scores when the job demands architectural outward change—tasks that rely on deep understanding of frameworks or novel code paths still expose weak corners.
This evolution aligns with broader trends in AI benchmarking. As these tools grow more powerful, moving past bug fixes into full feature work or system-level transformations is inevitable. Android Bench 2.0 reflects a more realistic developer workload. It shows which models are ready for heavy lifting, and which are still bound by simpler, deterministic tasks like translations of existing code structures. \
For teams, investors, and researchers, these results offer clearer pain points: dependency management, runtime validation, knowledge of newer Android frameworks, and architectural shifts. Watching how models evolve across Android Bench 2.0 will reveal where progress is real—and where hype still outpaces capability.
