Google delays Gemini 3.5 Pro release after disappointing coding performance in June training update

Google has pushed back the release of Gemini 3.5 Pro past its June deadline after coding performance failed to meet expectations, according to Bloomberg.

What happened

Google announced Gemini 3.5 Flash at I/O 2026 in mid-May and said the Pro version would arrive in June. That deadline has passed with no update.

According to Bloomberg, Google is "taking time to try to improve [Gemini 3.5 Pro's] capabilities, particularly in coding." In late June, Google updated the training data in an attempt to improve coding skills, but the results were disappointing.

The timeline suggests development saw a reset between I/O and the missed launch. Performance in other domains remains unclear. Gemini 3.1 Pro, the current flagship model, dates back to February 2026.

What Google says

In a statement, Google said it is "currently testing 3.5 Pro, an upgraded Flash model, and other models with partners." The company added: "We're shipping quickly across a wide range of models while keeping them highly cost-effective for customers."

No new timeline for Gemini 3.5 Pro's release was provided.

Internal coding context

As of April 2026, 75% of all new code at Google is AI-generated and approved by engineers, up from 50% last fall, according to Bloomberg. However, efforts face resistance from engineers who believe important code should be human-written to adhere to Google standards, according to ex-employees.

Internally, engineers are facing AI capacity restraints with coding tools. Google is working to "unite the company's internal artificial intelligence coding tools."

Google DeepMind (AI Studio), Cloud (Vertex), and the Android team (Android Studio) each maintain separate AI coding tool efforts.

What this means

The delay signals Google is prioritizing quality over speed for its flagship Pro model, particularly in coding—a domain where OpenAI and Anthropic have set high benchmarks. The fact that a late June retraining attempt failed suggests deeper architectural or data challenges rather than simple fine-tuning issues. With 75% of Google's own code now AI-generated, the company faces pressure to ship coding models that match internal standards while competing externally.