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

Table Extraction: Accurately parsing complex table structures (e.g., multi-row, multi-column, etc.) from document imagesChart Understanding: Converting charts and figures into structured machine-readable formats, summaries, or executable codeSemantic Key-Value Pair (KVP) Extraction: Identifying and grounding semantically meaningful key-value field pairs across diverse document layouts

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.

ChartNet uses a code-guided synthesis pipeline to generate 1.7 million diverse chart samples spanning 24 chart types and 6 plotting libraries. 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.

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.

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.

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.

Charts: Evaluated on the human-verified ChartNet benchmark using LLM-as-a-judge, Granite 4.0 3B Vision achieves the highest Chart2Summary (86.4%) score among all evaluated models, including significantly larger ones. It also ranks second on Chart2CSV (62.1%), behind only Qwen3.5-9B (63.4%), a model more than double its size.

Tables: We evaluate table extraction in two settings: cropped tables and full-page documents. The benchmark suite includes TableVQA-extract, OmniDocBench-tables, and PubTables-v2. Models are tasked with extracting tables in HTML format and scored using TEDS. 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.

Semantic KVP: VAREX is a benchmark specifically designed to discriminate between small extraction models, comprising 1,777 U.S. government forms. Granite 4.0 3B Vision achieves 85.5% EM accuracy zero-shot.

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.

Example Use Cases

Form Processing: Extract structured fields from invoices, forms, and receipts using KVP capabilities or generate natural‑language descriptions of figures. Financial Report Analysis: Use Docling to parse reports, detect figures, and crop visual elements. Process charts using Granite Vision’s chart2csv, chart2code, and tables using tables_json capabilities. Research Document Intelligence: Utilize Docling to handle OCR and layout parsing across dense academic PDFs, and pass extracted figures to chart2summary and table crops to tables_html.

Granite 4.0 3B Vision is available now on HuggingFace, released under the Apache 2.0 license.