If you code Android apps with AI, Google’s new benchmark makes it easier to pick the right model

For Android app developers relying on AI to code, picking the right model can be tricky. Not all models are built the same, and many are not specifically trained for Android development workflows. To address this, Google has introduced a new benchmark to help developers understand how well different AI models perform on real-world Android coding tasks.

Dubbed Android Bench, the new benchmark is designed to evaluate how well large language models (LLMs) handle typical Android development tasks. Google explains that the benchmark evaluates models using real-world tasks from public projects on GitHub and asks models to recreate actual pull requests and solve issues similar to what developers encounter while building Android apps. The results are then verified to see if they actually resolve the issue.

In simpler terms, the benchmark checks whether the code generated by AI models truly fixes the problem instead of just looking correct on the surface. This helps Google measure how useful different models really are when it comes to solving real Android development problems.

With the first version of Android Bench, Google planned “to purely measure model performance and not focus on agentic or tool use.” The results highlight a wide gap, with models successfully completing between 16% and 72% of the benchmark tasks. The company says publishing these results should make it easier for developers to compare models and pick the ones that are actually capable of handling real Android coding problems.

In addition to guiding developers, the benchmark could also push AI companies to improve their models’ understanding of Android development. To support that effort, Google has published Android Bench’s methodology, dataset, and testing framework on GitHub. Over time, this could lead to AI tools that are better equipped to navigate complex Android codebases and help developers build and fix apps more effectively.