Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs Nano Banana (Gemini 2.5 Flash Image)

At $3/M output tokens vs $2.5/M, you're paying 20% more for a model that scores 62/100 vs 50/100 - a 24% performance improvement. For production workloads generating millions of images daily, the break-even depends on whether your use case benefits from the new reasoning capability and doubled context window (66K vs 33K tokens), which enables processing longer image sequences or more complex multi-modal prompts.

The reasoning capability differentiator allows Nano Banana 2 to handle complex conditional image generation tasks that require logical inference - think generating diagrams based on textual descriptions or creating sequential visual narratives. This explains part of the 12-point score gap despite both models sharing vision, streaming, and JSON mode capabilities.

The jump from 33K to 66K tokens represents a generational architecture upgrade in Google's Flash series, allowing Nano Banana 2 to process approximately 50 high-resolution images in a single context versus 25 for the original. This 2x multiplier appears deliberately engineered rather than incremental, suggesting Google rebuilt the attention mechanism for their 3.1 generation specifically to handle longer multi-modal sequences.

The $0.5/M vs $0.3/M input pricing becomes negligible for most image generation use cases where prompts are short but outputs are large - a typical 500-token prompt costs $0.00025 vs $0.00015. However, for workflows involving image-to-image tasks with multiple input images, the 66K context window means you can batch process 2x more images per request, potentially offsetting the higher per-token cost through reduced API calls.