However, Fable 5 and GPT 5.5 also have extremely high operating costs, chewing through more than $130 in tokens for the 100-problem, 10-run benchmark. Gemini 3.1 Pro didn’t score as high, but it only costs $87 to run the test. Gemini 3.5 Flash, which is supposed to be cheaper to run than other models, has the highest cost on the leaderboard because it took so much longer to complete the benchmark: $165 per run and a 28-hour runtime.

The Android coding performance gap for Google’s models is a problem as the company shifts many of its projects toward agentic development. Obviously, Google would prefer that Android developers use Google’s tools in their workflows, which may be why Google has reportedly been offering to buy application source code from developers for AI training.

Community collaboration

Android Bench is supposed to evolve over time, adopting new workflows to test models. Google hopes that developers will want to contribute to Android Bench by sharing benchmarks and development tasks. To make that more feasible, Google is switching to the Harbor framework. According to the company, this testing sandbox makes it easy for developers to run, evaluate, and share results for Android Bench.

Google re-ran all its previous tests with Harbor to get a new baseline for LLM performance. So there has been some shift in the previously reported scores even though the underlying tests haven’t changed (yet). The historical data will remain online in an archive.

With the new, easier framework, developers can run their own development tasks against Android Bench and submit those for possible inclusion in the official test. The Android Bench GitHub has been updated with the new dataset and instructions on how to get involved.