Non-natural image gen and editing are difficult tasks. We tested the state of the art at the time — including Nano Banana 1.0 & GPT-image — all performed quite poorly on StructBench. Nano Banana 2 (NB2) just dropped, and its improvements strongly validate a direction we studied in StructBench 🤯 It achieves 90+ on our image generation tasks—by far the best we’ve seen 🔥 A few months before the release of Nano Banana 2, we introduced StructBench — a benchmark for evaluating models on non-natural images like diagrams, math figures, charts, and documents. Our motivation was simple: today’s image models are overly optimized for aesthetics, but struggle with factuality + structural reasoning. If we want truly unified multimodal models, the training mix needs non-natural data too. But NB2 still isn’t perfect: we still find failure cases where it misinterprets instructions or misses structural details. Excited to see the field moving toward models that reason as well as they render. Below, we provide some more analysis along with cool results! @GeminiApp @GoogleDeepMind