Google AI dropped one of its densest shipping weeks of the year, with six releases spanning enterprise image generation, multi-agent science, on-device multimodal models, and a live music instrument you can actually play. The throughline is a bet on agentic workflows and on-device intelligence, with most launches either generally available or open-weights from day one.
Nano Banana graduates from preview
Nano Banana 2 and Nano Banana Pro are now generally available through the Gemini Enterprise Agent Platform, Gemini API, and AI Studio. On May 28, Google DeepMind officially promoted both the gemini-3.1-flash-image-preview and gemini-3-pro-image-preview image models to General Availability, meaning developers no longer need to use temporary IDs with the -preview suffix in production environments. The preview versions will retain roughly a four-week buffer period after GA, with official retirement on June 25, so any production code still using the preview suffix must be updated before then to avoid 404 errors.
The dual-model strategy is now explicit: use Flash (Nano Banana 2) by default, and upgrade to Pro for scenarios involving text or branding. Pricing reflects that split, with Nano Banana Pro at $0.134 per 1K or 2K image and $0.24 for 4K, while Nano Banana 2 generates in under 2 seconds at roughly $0.02 to $0.04 per image. Batch and Flex pricing on Vertex cuts standard rates in half at the cost of async delivery, which adds up quickly for non-realtime workflows like nightly product image refreshes.
One quietly significant addition: Nano Banana 2 now supports video files as an input prompt, expanding multimodal conditioning beyond reference images. And the input context window for gemini-3.1-flash-image can reach 1 million tokens, which is rare for image generation models and lets you feed in large numbers of reference images and brand assets in a single call.
An idea tournament for science
Co-Scientist is Google's new multi-agent system aimed at the messiest part of research: generating hypotheses worth testing. Published in Nature, the multi-agent AI system is built with Gemini and iteratively generates, debates, and evolves novel hypotheses for complex scientific problems.
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