Gemini Beats Claude, GPT in Google’s First Android AI Coding Benchmark

Google just published its first Android Bench leaderboard, ranking the AI models that perform best at coding Android apps. 

Nine models made the list, all of which included tools from Google Gemini, Anthropic Claude, and OpenAI. Not surprisingly, Gemini 3.1 Pro Preview led the benchmark with a 72.4% score, followed by Claude Opus 4.6 and GPT-5.2 Codex. 

Google created the benchmark to measure how well AI systems solve real Android development problems using tasks drawn from several GitHub projects.

What is Android Bench and why is it important 

Google Developers launched Android Bench to provide a definitive metric for ranking AI tools by their performance on complex Android coding tasks. The benchmark evaluated AI models against real-world problems by using over 100 tasks drawn from nearly 39,000 GitHub pull requests. 

Google checks the tools’ ability to handle key Android development areas, such as Jetpack Compose for UI, asynchronous programming, Hilt for dependency injection, and Room for persistence. 

It also measures performance on issues developers commonly face, such as navigation migrations, build configuration, or changes from SDK updates. The methodology also covers advanced topics such as system UI, camera, media, foldable adaptations, and granular permissions. 

The data pool relied heavily on Java (71%) and Kotlin (25%), which remain two of the most-used programming languages for Android development.

Winners of the leaderboard

It isn’t surprising that Google’s own AI tool came first, given how much focus Google appears to be giving to AI coding. Below are the top-performing AI models, ranked in descending order, with their test scores in percentage:

• Gemini 3.1 Pro Preview: 72.4%
• Claude Opus 4.6: 66.6%
• GPT-5.2-Codex: 62.5%
• Claude Opus 4.5: 61.9%
• Gemini 3 Pro Preview: 60.4%
• Claude Sonnet 4.6: 58.4%
• Claude Sonnet 4.5: 54.2%
• Gemini 3 Flash Preview: 42.0%
• Gemini 2.5 Flash: 16.1%

Google Developers also recommended that users periodically check back for new leaderboard changes.

What does this leaderboard mean for AI platforms and developers?

AI is rapidly improving, driving an explosion of usage among individual developers and enterprise teams seeking to cut costs. With just a subscription to some AI coding apps, developers get access to different AI models built for coding.

Yet a central question remained unanswered: which AI performs best for coding, especially for Android development, a domain that, according to Google Developers, existing benchmarks overlook?

The Android Bench results immediately resolve this debate, providing developer teams with a reliable, narrow range of high-performing AI models. With this, teams are empowered to quickly select an AI solution that matches their budget and preference, and also eliminate the time and resources spent testing multiple options.

Crucially, the public benchmark provides a clear growth opportunity for competitor platforms. As a major industry player, Google established a transparent standard. 

By leaving their judgment metric and the list of GitHub repositories it ran the tests on out in the open, it gave other AI creators a way to assess which tool they must level up against. Also, this opens a way for these AI platforms to identify specific features they can improve on their own.

Also read: Google’s Gemini 3.1 Pro is gaining traction as the company pushes harder into advanced coding and reasoning workflows.

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