Google just made its Android-focused LLM leaderboard a lot harder to ignore. In the July 2026 release of Android Bench, the team behind the benchmark swapped the evaluation engine to the Harbor framework, added eight fresh models to the rankings, and opened the whole project up to community contributions. If you build Android apps and have been wondering which AI model is actually worth wiring into your IDE or your agent workflows, this update is your cue to start paying attention.
Android Bench launched back in March as Google's answer to a deceptively simple question: which LLMs can actually get real Android development work done? Instead of leaning on generic math trivia or abstract reasoning sets, the benchmark evaluates models on hands-on tasks like migrating screens to Jetpack Compose, fixing wearable networking code, and adapting apps to platform API changes. It measures what a model does inside a real Android project, not what it scores on a quiz.
What changed in the July release
The headline change is the move to the Harbor framework. Previously, Android Bench used mini-swe-agent v1, a general-purpose benchmarking agent that Google adapted to the nuances of Android development. Harbor is a more standardized, community-friendly framework that makes it easier for anyone to run the same benchmark, plug in their preferred setup, and share results. Because the evaluation engine changed, Google re-ran every model on the new harness, which means the baseline scores shifted slightly. Historical results remain available in the archive if you want to compare old and new numbers.
The second big change is the roster. Eight new models joined the leaderboard in this release:
- Claude Fable 5, Claude Sonnet 5, and Claude Opus 4.8 from Anthropic
- GLM 5.2 from Z.ai
- Kimi K2.7 Code from Moonshot
- MiniMax M3
- Qwen 3.7 Plus and Qwen 3.7 Max from Alibaba
Right now, Claude Fable 5 sits at the top of the overall board with a score of 84.5, ahead of GPT-5.5 at 80.2, with Claude Sonnet 5 in third place at 76.2. If you only care about open-weight models, GLM 5.2 leads that pack at 72.2, with Kimi K2.7 Code close behind at 70.4.
How to read the leaderboard like a developer
Raw scores only tell you part of the story. Before you pick a model for your daily workflow, check three things on the updated leaderboard:
- Score: the overall capability measure across the Android task set. Higher is better, but small gaps between adjacent models rarely translate into a noticeable difference in your day-to-day work.
- Cost and efficiency: Android Bench now tracks these dimensions, and they matter more than ever if you are running agents on a budget or planning on-device inference.
- Task coverage: look at how a model performs on the specific task types you actually care about, like Compose migrations or wearable networking, rather than just the aggregate number.
One thing to keep in mind: because Google re-baselined everything on Harbor, scores from the new release are not directly comparable to the March numbers. Treat the archive as history and the current leaderboard as your starting point for any decision.
Run your own benchmark and contribute tasks
The most practical part of this update is that you no longer have to sit on the sidelines. Google opened Android Bench to the community in two concrete ways. First, you can design and submit your own Android development tasks; the team reviews submissions and decides whether they get added to the official benchmark. Second, you can run the benchmark yourself and share the results, using either Google's dataset or your own custom tasks.
To get started, clone the public GitHub repository and read through the existing task list to understand the expected format. Write a task that reflects a real scenario you hit in your own work, ideally one that is hard to game with memorized answers. Submit it through the standard review flow, then head over to Harbor Hub to explore the dataset or run evaluations against your preferred models.
Why bother? Because benchmarks only stay useful while they reflect reality. If the global Android developer community floods Android Bench with tasks that mirror everyday struggles, the leaderboard becomes a genuinely reliable buying guide for AI coding assistants instead of another number to quote in a press release. That is the real payoff of this update, and it is one you can help shape.
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