Android Bench Standard Updates
Google has released a new update to the Android Bench list, the benchmark tool aimed at measuring the ability of artificial intelligence models to provide assistance in programming Android applications. This standard focuses on evaluating models based on specific programming requirements and best practices followed in system application development, while including the average operational cost for each model to help developers make informed economic and technical decisions.
Model Performance in Recent Tests
The latest update saw the addition of results for advanced models, with Anthropic's models topping the list, specifically Claude Sonnet 5.5, which achieved a success rate of 40% and a code completion rate of 80.9%. The Claude Opus 5.5 model also came in second place.
On the other hand, OpenAI's GPT-6.1 Sol model emerged as a notable option for developers; despite achieving a success rate of 29.3% and a completion rate of 82.8%, it is distinguished by a very low operational cost of $99.50, compared to the cost of the leading Claude model, which is $1,574.90.
Gemini 4 Argon's Position
The appearance of Google's new Gemini 4 Argon model was disappointing in this evaluation, as it placed fifth, lagging behind competing models such as Claude Fable 5.1. The model recorded a success rate of 21.3% and a completion rate of 81.8%, with an average cost of $267.70. This performance confirms previous internal reports that indicated difficulties the model faces in handling certain programming tasks, despite Google's continuous assertions regarding the model's efficiency and capabilities.
What This Means for Developers
The importance of these updates lies in providing greater transparency regarding how models perform in real-world work environments. For Android developers, technical performance alone is no longer the decisive criterion; cost-to-achievement has become a fundamental factor. This evaluation proves that market-leading models vary significantly in their specializations, forcing developers to choose the tool that balances code accuracy with the budget available for technical projects.