# Android Bench 2.0 Adds Long-Horizon Tasks and Agentic Evaluation for Android AI Coding

DevFeed: [Android Bench 2.0 Adds Long-Horizon Tasks and Agentic Evaluation for Android AI Coding](<https://devfeed.tech/articles/android-bench-2-0-pushing-the-frontier-with-challenging-long-horizon-tasks-42803.md>)

Original publisher: [Read original article](<http://android-developers.googleblog.com/2026/09/android-bench-2-long-horizon-tasks.html>)

Author: Android Developers (noreply@blogger.com)

Published: 2026-09-16T15:58:00Z

Content type: release

Language: en

Sources: [Android Developers Blog](<https://devfeed.tech/sources/android-developers-blog-3.md>)

Topics: [Android](<https://devfeed.tech/topics/android.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Large Screen](<https://devfeed.tech/topics/large-screen.md>), [Jetpack Compose](<https://devfeed.tech/topics/jetpack-compose.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [agentic-android-development](<https://devfeed.tech/tags/agentic-android-development.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [android](<https://devfeed.tech/tags/android.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [coding](<https://devfeed.tech/tags/coding.md>), [coding-agents](<https://devfeed.tech/tags/coding-agents.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>)

## AI overview

Android Bench 2.0 introduces long-horizon Android development tasks and agentic evaluation for assessing large language models and coding agents. Its continuous scoring considers functionality, visual fidelity, regressions, instruction adherence, and structural constraints.

## Source excerpt

Posted by Matthew McCullough, VP, Product Management, Android Developer When we first launched Android Bench, we built a rigorous foundation for evaluating how large language models (LLMs) assist developers with real-world Android tasks. As AI models and agents rapidly evolve, we've been updating our methodology, such as aligning our benchmark framework with the Harbor framework. Today we're releasing the first set of long-horizon tasks (LHT), which are tasks of great complexity that take an engineer multiple days or even a week to complete. We are also introducing agentic evaluation, starting with agents from corresponding model providers. This addition brings us to Android Bench 2.0--a major upgrade designed to evaluate AI models and agents against the scale, ambiguity, and complex multi-step problem solving that you tackle every day. The Android Bench 2.0 leaderboard From incremental fixes to long-horizon tasks The first iteration of Android Bench, along with similar early AI coding benchmarks, focused on incremental changes to existing repositories, in many cases limited to bug fixes or smaller feature requests. This was a reflection of the capabilities of AI assistance at the time, as well as how you were using it. To continue helping you find the models and coding agents best suited to your development workflow, we have raised the bar of our evaluations to match the work you delegate to AI. Android Bench 2.0 mirrors these ambitious challenges with LHTs that include upgrading dependencies, adding new features, building apps from scratch, or converting a cross-platform app to Android. Complex tasks require a more nuanced evaluation and scoring On multi-day engineering tasks, binary pass or fail grading doesn't capture the full picture. For example, an agent might refactor 40 screens to Jetpack Compose, set up database tables, and pass 90% of requirements, but fail a single edge-case assertion. Binary scoring rates this run as 0%, obscuring the model's architectur