Evaluating how well artificial intelligence can write code used to be straightforward: you gave a model a self-contained bug, ran a test, and marked it as a pass or a fail. However, as developers push AI tools to handle actual heavy lifting, those binary tests no longer tell the full story. To close that gap, Google released Android Bench 2.0, completely shifting how it measures coding assistance by putting models through multi-day engineering challenges.
The updated benchmark moves away from quick refactoring jobs and focuses on “long-horizon tasks.” Instead of fixing small functions, models now attempt work that would take a human software engineer several days or even a full week. This includes building applications from scratch, executing library migrations, or converting cross-platform apps directly to Android.
Replacing binary grades with continuous scoring
This jump in complexity quickly broke the original grading system. Under the old pass/fail setup, an AI model that successfully completed 90% of a massive codebase migration would still receive a zero. The approach offered zero insight into its actual progress.
Android Bench 2.0 replaces that binary metric with a continuous completion rate. Google calculates the new score by evaluating functionality, visual fidelity, and whether the model introduced regressions, while penalizing deviations from structural instructions.
The change in difficulty was immediate. While models regularly scored above 90% on the original benchmark, top-tier performance cratered under the new setup. OpenAI’s GPT-6 Astra currently leads the updated leaderboard, but it caps out at just a 28% pass rate.
Where models excel and where they stall
The benchmark results highlight a sharp divide in what current AI coding models can actually handle. Straightforward, deterministic transformations—such as converting Java to Kotlin or swapping Retrofit for Ktor—went smoothly across various model tiers. AI also performed surprisingly well when writing brand-new features from scratch.
However, complex architectural tasks are still a big stumbling block. The models had issues with runtime validation, framework changes that broke things and libraries that never shipped. Porting cross-platform apps to Android proved especially difficult, with even frontier models capping out at an 80% completion rate and no model reaching 100%.
Evaluating the whole agent package
Android Bench 2.0 also introduces agentic evaluations. Instead of testing standalone models in isolation, Google paired systems with their corresponding developer harnesses—such as Gemini 3.8 Flash running on Google Antigravity or GPT-5.6 Sol on Codex.
Google noted that harness design heavily influences overall token efficiency and developer outcomes. By testing agents together with their environments, the benchmark provides a much clearer picture of how these tools perform in real-world development workflows.
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