New Google AI Models for Android App Coding: Rankings Revealed

Google has refreshed its “Android Bench” ranking of AI models for Android app development, adding several new open-weight models and publishing detailed metrics on token usage, latency, and cost. The updated benchmark gives developers a clearer view of not only which models perform best on Android development tasks, but also how efficiently and cheaply they operate.

Large language models have become remarkably capable at coding, increasingly assisting developers with app creation, debugging, and implementation of best practices. Earlier this year, Google launched the Android Bench to evaluate models against common Android development tasks and to measure how well each model follows recommended patterns and practices.

When Android Bench first appeared, Gemini 3.1 Pro led the rankings, and OpenAI’s GPT 5.4 later matched that top position. In the most recent May 18, 2026 update, Google shows a new leader: GPT 5.5. According to the benchmark, GPT 5.5 narrowly outperforms GPT 5.4 and Gemini 3.1 Pro by roughly two percent.

Crucially, this update also adds more context by reporting three operational metrics for each model:

Average Latency: Measured as the time required to complete 100 benchmark tasks across 10 runs.
Average Total Tokens: Total token consumption for a full benchmark run, averaged across 10 runs.
Average Cost: Estimated cost per benchmark run in US dollars at the time of testing.

Those additional metrics make it easier to compare models not only by raw capability but also by speed, token efficiency, and cost-effectiveness. For example, while GPT 5.5 tops the leaderboard in score, it also has a notably higher average cost—more than twice that of Gemini 3.1 Pro for the same benchmark run—highlighting the trade-offs teams should consider when choosing a model for production use.

Below are the top ten models in the May 21, 2026 ranking, including the new columns for latency, tokens, and cost:

Model	Score	Avg Latency	Avg Total Tokens	Avg Cost
New: GPT 5.5	74	15.5	64.5	$133.9
GPT 5.4	72.4	21.2	64.2	$91.7
Gemini 3.1 Pro Preview	72.4	11.5	75.4	$49.0
New: Claude Opus 4.7	68.7	11.6	90.0	$124.3
GPT 5.3 Codex	67.7	11.2	71.4	$42.6
Claude Opus 4.6	66.6	9.9	69.5	$84.4
GPT 5.2 Codex	62.5	24.3	124.4	$121.9
Claude Opus 4.5	61.9	12.5	79.8	$102.5
Gemini 3 Pro Preview	60.4	9.8	117.0	$63.7
New: GLM 5.1	59.7	33.4	80.2	$46.7

The updated ranking also introduces a larger set of open-weight models, such as Gemma, Qwen, DeepSeek, and MiMo. Among these open-weight contenders, GLM 5.1 achieved the highest score in the expanded list, followed by Kimi K2.6. The inclusion of more open-weight models helps developers assess alternatives that may offer better cost, licensing, or deployment flexibility compared with closed models.

Google updates Android Bench roughly every month, reflecting rapid progress in model capabilities and new releases. With Google’s own Gemini 3.5 Pro on the horizon and Gemini 3.5 Flash already available, the landscape may shift again as vendors refine performance, latency, and pricing.

For teams choosing an AI model for Android development, these benchmark details emphasize three important considerations: raw developer-assist capability, runtime performance (latency), and practical cost per task. Evaluating all three allows teams to balance speed, accuracy, and budget when integrating AI into their development workflows.

Are you using AI models for Android app development? If so, which model do you prefer and why?

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