granite-vision-4.1

About

What granite-vision-4.1 is

Granite-Vision-4.1-4B Model Summary: Granite Vision 4.1 4B is a vision-language model (VLM) that delivers frontier-level performance on structured document extraction tasks — chart extraction, table extraction, and semantic key-value pair extraction — in a compact 4B parameter footprint, providing a lightweight alternative to much larger frontier models for these tasks: - Chart extraction: Converting charts into structured, machine-readable formats (Chart2CSV, Chart2Summary, and Chart2Code) - Table extraction: Accurately extracting tables with complex layouts from document images to JSON, HTML, or OTSL - Semantic Key-Value Pair (KVP) extraction: Extracting values based on key names and descriptions across diverse document layouts The model is finetuned on top of Granite-4.1-3B, with a 3.4B LLM and 0.6B Vision Encoder and Projectors. See Model Architecture for details. The methodology and data (ChartNet) used for this model are described in the paper ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding. While our focus is on specialized document extraction tasks, the current model preserves and extends the capabilities of Granite Vision 4.0 3B, ensuring that existing users can adopt it seamlessly with no changes to their workflow. It continues to support vision‑language tasks such as producing detailed natural‑language descriptions from images (image‑to‑text). The model can be used standalone and integrates seamlessly with Docling to enhance document processing pipelines with deep visual understanding capabilities. - Developer: IBM Research - GitHub Repository: https://github.com/ibm-granite - Release Date: April 29th, 2026 - License: Apache 2.0 Supported Tasks The model supports specialized extraction tasks, each activated by a simple task tag in the user message. The chat template automatically expands tags into the full prompt — no need to write verbose instructions. Model Performance Benchmark Results Granite Vision 4.1 4B provides a lightweight alternative to frontier models on structured document extraction benchmarks, delivering comparable performance at a fraction of the parameter count. Chart Extraction We evaluate ch

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

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How it works

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Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys granite-vision-4-1 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (granite-vision-4-1 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"granite-vision-4-1","messages":[{"role":"user","content":"Hello"}]}'

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