New Android Bench Results Compare AI Coding Agent Costs
Updated performance data allows developers to measure AI success rates for complex mobile engineering tasks.
Updated on Oct. 27, 2026 in Artificial Intelligence
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Google has updated its Android Bench 2.0 leaderboard with performance data for new coding agents, including models from Anthropic, OpenAI, and Google. The benchmark evaluates AI capabilities on complex, long-running Android engineering tasks using specialized agents.
Why it matters
These results provide standardized performance metrics for AI models tackling enterprise-grade mobile software development. By distinguishing between task pass rates and partial completion, the leaderboard helps developers gauge which models can reliably handle end-to-end coding requirements.
Claude Sonnet 5.5 achieved a 40% pass rate and 80.9% completion rate, while GPT-6.1 Sol cost $99.50 to run through the benchmark. Gemini 4 Argon, using the Antigravity SDK, hit a 21.3% pass rate with an 81.8% completion score.
The players
A global technology company that manages the Android operating system and maintains software development benchmarks.
Anthropic
An AI research lab known for its Claude family of large language models used here for automated software engineering.
OpenAI
A developer of foundation models, represented here by its GPT-6 series and Codex agent systems.
The details
Android Bench 2.0 uses automated agents to execute complex, multi-step engineering tasks within the mobile operating system. A pass rate indicates the model successfully met all specified functional requirements for a given task, while completion scores track how far the model progressed through the codebase before failing. These metrics highlight how different architectures handle long-running dependencies in a software environment.
Timeline
September 2026: Google announced Android Bench 2.0.
October 26, 2026: Google updated the Android Bench leaderboard.
The Tech Race
This leaderboard update expands the Android Bench 2.0 project by pitting proprietary models against each other in real-world mobile coding environments. It marks a shift toward measuring agentic success rates in software development rather than simple token generation.
Developers and companies can use these benchmarks to decide which AI agents offer the best cost-to-performance ratio for automated mobile coding tasks. The leaderboard data suggests that model choice significantly impacts both budget efficiency and the likelihood of successful code completion.
The takeaway
These findings underscore that high completion rates do not always equate to functional code parity in automated workflows. Engineers should monitor future leaderboard iterations for shifts in pass rates as models move from beta development to production integration.
Further reading
For more on the latest research and benchmarks in the field, visit Artificial Intelligence.
Source note: This article includes information reported by Techgenyz.
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