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Donut DocVQA is an AI model released 2022 by naver-clova-ix, a leading organization in large language model research. Naver Clova's Donut — OCR-free document understanding. Answers natural-language questions about scanned PDFs, receipts, and forms. With ~200M and a a large context window context window, Donut DocVQA is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.

Donut DocVQA uses a dense transformer architecture with ~200M of parameters, all of which are active for every input token. Dense architectures provide predictable, consistent behavior across diverse task types — there are no "cold" experts to warm up, and quality is uniform across prompts. naver-clova-ix's training pipeline combined large-scale pretraining on DocVQA and document understanding datasets with supervised fine-tuning and alignment techniques (RLHF or DPO), producing Donut DocVQA's characteristic balance of capability, helpfulness, and safety.

As of 2026-10-16, Donut DocVQA by naver-clova-ix demonstrates measurable performance on standard AI evaluation benchmarks. On DocVQA — which measures model performance on specialized evaluation tasks — it achieves 67.0%. These scores place Donut DocVQA — at ~200M of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.

The primary strengths of Donut DocVQA are: No-OCR document parsing — reads documents directly as images; End-to-end: eliminates OCR error propagation; MIT license — free commercial use; Strong on structured forms and invoices. These make it particularly well-suited for Form processing pipelines without OCR dependencies, Invoice and receipt data extraction, Document QA on structured business documents. Users working on complex, multi-step problems benefit most from Donut DocVQA's ability to maintain coherence across long inputs and produce structured, verifiable outputs.

Like all current AI systems, Donut DocVQA has limitations: 67% DocVQA lags behind newer document AI models (90%+ for modern models); Knowledge cutoff 2022. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. naver-clova-ix continues to improve Donut DocVQA through ongoing research.

Donut DocVQA is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making Donut DocVQA accessible to students, researchers, developers, and early-stage teams globally.

As of 2026-10-16, Donut DocVQA by naver-clova-ix demonstrates measurable performance on standard AI evaluation benchmarks. On DocVQA — which measures model performance on specialized evaluation tasks — it achieves 67.0%. These scores place Donut DocVQA — at ~200M of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.

Donut: Document Understanding Transformer, end-to-end (no OCR) document parsing

The training pipeline for Donut DocVQA involves multiple stages: large-scale pretraining on DocVQA and document understanding datasets to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. naver-clova-ix's research team — based in South Korea — has optimized Donut DocVQA for the specific capability profile described above.

Inference efficiency: Donut DocVQA at ~200M is designed to deliver quality outputs at practical serving speeds. Its context capacity supports complex multi-turn conversations without quality degradation.

Getting started with Donut DocVQA on LITHOVEX AI takes under two minutes. Follow these steps:

For best results with Donut DocVQA: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.

naver-clova-ix is a leading organization in AI research and large language model development. Their team has built expertise across natural language processing, model architecture design, and alignment research. naver-clova-ix's models are used by researchers, developers, and enterprises worldwide. Their commitment to advancing AI capability while maintaining responsible deployment practices has made them a respected contributor to the global AI ecosystem. The organization continues to publish research and release models that push the boundaries of what AI systems can achieve.

Founded: 2021, headquartered in South Korea.

Visit the official naver-clova-ix website →

Donut DocVQA by naver-clova-ix competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.

Both Donut DocVQA and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.

Donut DocVQA is released under MIT, which means you can download and run it on your own hardware. Below are the quickest ways to get started.

Naver's Donut model challenges the traditional OCR+NLP pipeline with a direct image-to-answer approach. Its OCR-free design reduces error accumulation from separate OCR stages — valuable for Korean-language documents and international document intelligence applications globally.

Donut DocVQA by naver-clova-ix has the following version history and notable updates:

For the complete changelog and latest release notes, visit the official naver-clova-ix model page.