The model ships as a LoRA adapter on top of Granite 4.0 Micro, our dense language model, keeping vision and language modular for text-only fallbacks and seamless integration into mixed pipelines. It continues to support vision-language tasks such as producing detailed natural-language descriptions from images (e.g., “Describe this image in detail”). The model can be used standalone or in tandem with Docling to enhance document processing pipelines with deep visual understanding capabilities.

Granite 4.0 3B Vision’s performance is the result of three key investments: A purpose-built chart understanding dataset constructed via a novel code-guided data augmentation approach, a novel variant of the DeepStack architecture that enables high-detail visual feature injection, and a modular design that keeps the model practical for enterprise deployment.

Charts present a challenge for vision-language models (VLMs) because understanding them requires jointly reasoning over visual patterns, numerical data, and natural language, a combination most VLMs cannot handle well, especially when spatial precision matters—such as reading exact values off a line chart. To close this gap, we’ve developed ChartNet: a million-scale multimodal dataset purpose-built for chart interpretation and reasoning, described in detail in our upcoming CVPR 2026 paper.

7 million diverse chart samples spanning 24 chart types and 6 plotting libraries [see Figure 1]. What makes it so distinctive is that each sample consists of five aligned components—plotting code, rendered image, data table, natural language summary, and QA pairs—providing models a deeply cross-modal view of what a chart means, not just what it looks like.

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