Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents

Today we're excited to announce Granite 4.0 3B Vision , a compact vision-language model (VLM) designed for enterprise document understanding. It’s purpose-built for reliable information extraction from complex documents, forms, and structured visuals. Granite 4.0 3B Vision excels on the following capabilities:

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.

Most VLMs inject visual information into their language model at a single point, which forces the model to handle both high-level semantics and fine-grained spatial detail simultaneously. Granite 4.0 3B Vision takes a different approach with DeepStack Injection: abstract visual features are routed into earlier layers for semantic understanding, while high-resolution spatial features are fed into later layers to preserve detail. The result is a model that understands both what is in a document and where—which is critical for tasks like table extraction, chart understanding, and KVP parsing where layout matters as much as content. For a full technical breakdown, see the Model Architecture section of the model card.

ChartNet uses a code-guided synthesis pipeline to generate 1.7 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. The dataset also includes human-annotated and real-world subsets, filtered for visual fidelity, semantic accuracy, and diversity.

Tables : We evaluate table extraction in two settings: cropped tables (isolated regions) and full-page documents (tables embedded in complex layouts) [see Figure 3]. The benchmark suite includes TableVQA-extract (cropped table images), OmniDocBench-tables (full-page documents), and PubTables-v2 (both cropped and full-page settings). Models are tasked with extracting tables in HTML format and scored using TEDS, a metric that captures both structural and content accuracy. Granite 4.0 3B Vision achieves the strongest performance across benchmarks, leading on PubTablesV2 on both cropped (92.1) and full-page (79.3), OmniDocBench (64.0), and TableVQA (88.1) scores among all evaluated models.

Granite 4.0 3B Vision can operate either as a stand‑alone visual information extraction engine or as part of a fully automated document‑processing pipeline with Docling. The model is designed to support scalable, accurate extraction across diverse document types and visual formats.

2. Integrated Document Understanding Pipeline With Docling Granite 4.0 3B Vision can also be integrated seamlessly with Docling to support complete end‑to‑end document understanding. This mode can offer:

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 .

The result is a training resource that moves VLMs from merely describing charts to genuinely understanding the structured information they encode—with consistent gains across model sizes, architectures, and tasks.

Granite 4.0 3B Vision is packaged as a LoRA adapter on top of Granite 4.0 Micro , rather than as a standalone model. In practice, this means the same deployment can serve both multimodal and text-only workloads, automatically falling back to the base model when vision isn’t required. This keeps enterprise integration straightforward without sacrificing performance.