Text rendering inside images is one of the historically weak areas of image generation models, and Nano Banana 2 is not an exception to this pattern. For short, simple text strings in Latin-script languages—single words, short labels, or brief UI copy—the model produces legible output with reasonable consistency. Accuracy degrades as the text gets longer, as the font style becomes more specific, or as the prompt calls for precise placement of text within the composition. Common failure modes include misspelled words, inconsistent character shapes, and text that drifts from its intended position within the image.
For non-Latin scripts and local languages, the quality varies significantly by script. Scripts with complex glyph systems, ligatures, or right-to-left directionality tend to produce less reliable output than simpler alphabetic systems. If your application needs to render multilingual text within images—for example, localized marketing assets or interface mockups for different regional markets—it is more reliable to generate the image without embedded text from Nano Banana 2 and add the text layer programmatically using a graphics library like Pillow, Sharp, or a canvas-based renderer on the frontend. This approach gives you full typographic control and consistent results regardless of the language.
The Pro tier of the model family has measurably better text rendering than Nano Banana 2, particularly for longer strings and non-Latin scripts, but neither tier is a replacement for purpose-built text rendering. The practical recommendation for any web application that needs precise multilingual text in images is to treat text as a compositing layer rather than a generation target, and reserve Nano Banana 2 for the background, illustration, or non-text visual elements of the image.