Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs Stable Diffusion 3.5

Nano Banana 2's 62/100 score reflects its multimodal architecture that accepts both text and image inputs with a 66K token context window, while Stable Diffusion 3.5's 17/100 score comes from its text-only input pipeline with 0 token context. The scoring gap widens because Nano Banana 2 includes Vision, Streaming, JSON Mode, and Reasoning capabilities that enable complex image manipulation workflows beyond simple text-to-image generation.

The 11,666.7x price differential reflects fundamentally different deployment models: Stable Diffusion 3.5 is open source, meaning the $35,000/M represents API hosting costs that users can eliminate entirely by self-hosting. Nano Banana 2's closed-source nature locks users into Google's $0.50/M input and $3/M output pricing, making Stable Diffusion 3.5 potentially cheaper for high-volume users who can manage their own infrastructure.

Nano Banana 2's text+image->text+image modality enables iterative image editing workflows where users can feed modified images back for further refinement across its 66K token context, while Stable Diffusion 3.5 requires starting from scratch with each text prompt. This multimodal advantage particularly matters for applications like style transfer, image captioning with regeneration, or maintaining visual consistency across multiple related generations.

Migration only makes sense for teams spending less than $257/month on compute (based on the 11,666.7x price ratio), as self-hosted Stable Diffusion 3.5 becomes cheaper beyond that threshold. Teams should also consider that switching from an open-source model (Stable Diffusion 3.5) to a proprietary one (Nano Banana 2) introduces vendor lock-in and eliminates fine-tuning possibilities, despite gaining access to Nano Banana 2's superior JSON Mode and Streaming capabilities.