NuExtract3

About

What NuExtract3 is

🖥️ <a href="https://nuextract.ai/"API / Platform</a&nbsp;&nbsp; | &nbsp;&nbsp; 📑 <a href="https://numind.ai/blog"Blog</a&nbsp;&nbsp; | &nbsp;&nbsp; 🗣️ <a href="https://discord.gg/3tsEtJNCDe"Discord</a&nbsp;&nbsp; | &nbsp;&nbsp; 🛠️ <a href="https://github.com/numindai/nuextract"GitHub</a NuExtract3 is a unified 4B vision-language reasoning model for document understanding. It combines strong structured information extraction with high-quality image-to-Markdown conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. Try it out in the 🤗 space! Overview - Structured extraction: input (text/images) + JSON template + instructions -- JSON output - Markdown conversion: input (text/images) -- Markdown - Multimodal inputs: text, images, or text + images. - Multilingual documents. - Reasoning and non-reasoning inference modes. - Template generation for structured extraction from natural language or input document. Benchmark results Structured Extraction We benchmarked NuExtract on NuMind's internal structured benchmark, measuring model's performances on ~600 documents of diverse types including invoices, movie posters or floor plans. These documents and their ground-truth cover diverse use-cases testing model visual understanding, OCR, reasoning and ability to handle long input and output contexts. We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction. To measure a pair of predicted and ground-truth JSONs, we represent both as trees which we align based on node names, compute metric scores for aligned leaves and report the average of these scores. string and verbatim-string leaves are evaluated with indel distance (i.e. Levenshtein without replacement), while all others are evaluated with exact-match. Models were evaluated using vllm, with a temperature of 0.25 and a maximum of 65000 output token (for both thinking and answer), which largely exceeds 22000 which is the n

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

Specifications

What it is

How it works

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Variants

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 nuextract3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nuextract3 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":"nuextract3","messages":[{"role":"user","content":"Hello"}]}'

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