Nano Banana 2 can generate images that resemble technical flowcharts and schematics, but the output is visual rather than semantically structured. The model produces a raster image that looks like a diagram based on the visual patterns it learned during training. It does not generate machine-readable diagram formats such as SVG with correctly connected nodes, valid XML for drawing tools, or structured data that downstream tools can interpret. The shapes, arrows, and labels in the output are pixels that approximate the appearance of a diagram, not objects with spatial relationships that can be manipulated programmatically.
For use cases where the generated diagram only needs to be visually legible—concept sketches, presentation slides, brainstorming aids, or rough wireframes that will be redrawn by a human—Nano Banana 2 is a practical tool. It generates recognizable diagram imagery quickly and can adapt to different visual styles (whiteboard sketch, clean vector-style, hand-drawn). The reasoning layer in Nano Banana 2 helps with small diagrams where node count and connectivity need to match the prompt specification, though accuracy degrades as structural complexity increases, as discussed separately.
For applications where the generated diagram needs to be technically correct—infrastructure documentation, formal process models, engineering drawings, or any output that will be used as a reference for implementation—generating the diagram programmatically is the more reliable approach. Libraries like Mermaid, Graphviz, and D2 can produce accurate diagrams from structured text input, and purpose-built tools like draw.io or Lucidchart handle the layout and editing workflow. Nano Banana 2 is better positioned as a tool for generating illustrative context around technically precise diagrams than as a replacement for diagram authoring tools.