OpenAI's GPT 5.4 Ties Gemini 3.1 Pro at Top of Google's Android Coding Benchmark

Google's Android Bench—introduced in March 2026 as a resource for evaluating AI models in Android app development—released its first update today, showing OpenAI's latest model matching Google's flagship offering.

Benchmark Scores

GPT 5.4 and Gemini 3.1 Pro Preview both scored 72.4%, the highest on the list. OpenAI's GPT 5.3-Codex follows at 67.7%. Anthropic's Claude Opus 4.6 ranks fourth at 66.6%.

Complete April 2026 rankings:

• GPT 5.4: 72.4% (new)
• Gemini 3.1 Pro Preview: 72.4%
• GPT 5.3-Codex: 67.7% (new)
• Claude Opus 4.6: 66.6%
• GPT-5.2 Codex: 62.5%
• Claude Opus 4.5: 61.9%
• Gemini 3 Pro Preview: 60.4%
• Claude Sonnet 4.6: 58.4%
• Claude Sonnet 4.5: 54.2%
• Gemini 3 Flash Preview: 42%
• Gemini 2.5 Flash: 16.1%

Methodology

Google's evaluation framework assesses model capabilities across Android development essentials: Jetpack Compose for UI construction, Coroutines and Flows for asynchronous programming, Room for data persistence, and Hilt for dependency injection. Testing of OpenAI's models occurred in mid-March 2026, prior to their public release this week.

Important Caveats

Google explicitly noted that benchmark results should not be treated as definitive. Real-world performance varies significantly based on workflow, pricing, integration ease, and specific use cases. The methodology measures controlled scenarios that may not reflect production development conditions.

The remainder of the ranking remained unchanged from the initial late-February test run, with no new models added beyond OpenAI's two entries.

Context

Google positioned Android Bench as a tool to help developers "be more productive" and ultimately "deliver higher quality apps across the Android ecosystem." The benchmark represents Google's effort to provide transparent model comparisons for a specific, high-impact use case.

What This Means

The tight tie between GPT 5.4 and Gemini 3.1 Pro suggests convergence at the frontier for code-generation tasks. Developers choosing between them will likely base decisions on factors beyond benchmark scores—pricing, API latency, context window size, and ecosystem integration. The substantial performance gap between top-ranked models (72.4%) and older versions (Gemini 2.5 Flash at 16.1%) indicates rapid model improvement in specialized coding tasks. For teams standardized on either Google or OpenAI infrastructure, this benchmark provides useful calibration without necessarily indicating a decisive winner.