Google’s “Android Bench,” a ranking system for AI models used in Android app development, has been updated. In the latest update, OpenAI’s newest model is now tied with Google’s Gemini for the top position.
First released in March, the Android Bench is a resource designed to help developers compare AI models for coding Android apps. Google evaluates models on practical developer tasks and Android-specific technologies, including Jetpack Compose for building user interfaces, Kotlin Coroutines and Flows for managing asynchronous work, Room for local persistence, and Hilt for dependency injection. These criteria reflect common patterns in modern Android development and aim to show how well each model supports real-world app workflows.
In this update, Google added two OpenAI models—GPT 5.4 and GPT 5.3 Codex—which moved quickly toward the top of the rankings. The update demonstrates how rapidly model performance can change as vendors release new versions tuned for coding tasks.
Best AI for Android app development, according to Google (4/9/26)
- New: GPT 5.4: 72.4%
- Gemini 3.1 Pro Preview: 72.4%
- New: GPT 5.3-Codex: 67.7%
- 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%
- Gemini 2.5 Flash: 16.1%
Most of the other rankings remain unchanged from the initial run, which used results from late February. OpenAI’s newest models were evaluated in mid-March before the publication of this update. These numbers represent a snapshot based on Google’s testing methodology and the set of tasks chosen for the bench.
It’s important to treat benchmark results like these as directional rather than definitive. Benchmarks provide a controlled environment to compare models on specific tasks, but real development work introduces many variables: your codebase structure, team practices, the specific Android APIs you use, project constraints, and the way you integrate AI assistance into your workflow. A model that performs well on a benchmark might still require prompt engineering or additional validation to be productive and safe in your specific context.
Google has stated that the aim of publishing the Android Bench is to help developers become more productive and to encourage higher quality apps across the Android ecosystem. By showing relative strengths and weaknesses, the bench can guide teams in selecting models that align with their priorities—whether that’s UI generation with Jetpack Compose, managing asynchronous flows reliably, or producing maintainable persistence and dependency injection code.
What developers should consider
When choosing an AI model for Android development, consider these practical factors:
- Task fit: Some models are stronger at generating UI code, while others excel at backend logic or state management. Align model choice with the tasks you rely on most.
- Integration: How the model integrates into your development environment, CI/CD pipeline, and code review process will affect overall productivity.
- Reliability and correctness: Generated code should be reviewed and tested. Benchmarks don’t eliminate the need for human oversight.
- Security and privacy: Evaluate model behavior around sensitive data, API keys, and proprietary code before integrating into production systems.
- Cost and latency: Performance characteristics and pricing will influence feasibility for continuous use in a team setting.
Benchmarks like the Android Bench are useful tools, but they are one input among many when selecting AI tooling. Developers should run small pilots, measure outcomes on their own codebases, and iterate on prompts and integration patterns to find the best fit.
More on Android:
- Play Store search improvements now include app review search features and interface changes to filters.
- Google previewed a compact AI runtime for Android, intended to bring on-device capabilities to more apps later this year.
- Google is rolling out a new verification app for Android developers, and has shared a timeline for verification changes affecting developer workflows.
If you build Android apps, use this updated bench as a starting point to evaluate models, but validate performance and fit on real tasks and code. The landscape of AI models for development is evolving quickly, and regular testing will help teams pick the right tools as capabilities change.