VARCO-VISION-2.0 is a multimodal AI model capable of understanding both images and text to answer user queries. It supports multi-image inputs, enabling effective processing of complex content such as documents, tables, and charts. The model demonstrates strong comprehension in both Korean and English, with significantly improved text generation capabilities and a deeper understanding of Korean cultural context. Compared to its predecessor, performance has been notably enhanced across various benchmarks, and its usability in real-world scenarios—such as everyday Q&A and information summarization—has also improved.

In addition to the 14B full-scale model, a lightweight 1.7B version is available for on-device use, making it accessible on personal devices such as smartphones and PCs. VARCO-VISION-2.0 is a powerful open-source AI model built for Korean users and is freely available for a wide range of applications.

🚨News🎙️

👀 We are going to release VARCO-VISION-2.0-1.7B-OCR soon!
👀 We are going to release VARCO-VISION-2.0-1.7B soon!
📰 2025-07-16: We released VARCO-VISION-2.0-14B at link
📰 2025-07-16: We released GME-VARCO-VISION-Embedding at link

Key Features

Multi-image Understanding: Newly added support for multi-image inputs enables the model to analyze multiple images simultaneously and make more holistic and context-aware decisions.
Korean Language Specialization: The model is further specialized for Korean, with a deeper understanding of Korean language, context, and culture. Korean text generation has been significantly improved, resulting in more natural, fluent, and accurate responses.
OCR with Text Localization: Unlike typical models that only recognize and generate text from images, VARCO-VISION-2.0 can also identify the position of the text and provide bounding boxes around it. This makes it especially useful for document understanding, signage interpretation, and structured visual data.
Enhanced Safety: Improved robustness and filtering to ensure safer handling of harmful or sexually explicit content.

VARCO-VISION-2.0 Family

Model Name	Base Models (Vision / Language)	HF Link
VARCO-VISION-2.0-1.7B	siglip2-so400m-patch16-384 / Qwen3-1.7B	link
VARCO-VISION-2.0-14B	siglip2-so400m-patch16-384 / Qwen3-14B	link
VARCO-VISION-2.0-1.7B-OCR	siglip2-so400m-patch16-384 / Qwen3-1.7B	link
GME-VARCO-VISION-Embedding	Qwen2-VL-7B-Instruct	link

Model Architecture

VARCO-VISION-2.0 follows the architecture of LLaVA-OneVision.

Evaluation

We adopted benchmark scores directly from OpenVLM Leaderboard where available, and conducted our own evaluations for benchmarks not included in OpenVLM Leaderboard, comparing results against various open-source models to provide a fair and comprehensive evaluation. Please note that for certain benchmarks involving LLM-based evaluation (e.g., LLaVABench), results may not be exactly reproducible due to variations in the underlying LLM behavior.

English Benchmark

Benchmark	InternVL3-14B	Ovis2-16B	Qwen2.5-VL-7B	VARCO-VISION-2.0-14B
MMStar	68.9	67.2	64.1	64.8
SEEDBench_IMG	77.5	77.7	77.0	78.3
LLaVABench	84.4	93.0	91.0	90.0
OCRBench	877	879	888	863

Korean Benchmark

Benchmark	InternVL3-14B	Ovis2-16B	Qwen2.5-VL-7B	VARCO-VISION-2.0-14B
K-MMStar	64.9	29.7	49.3	63.3
K-SEED	78.2	73.2	75.7	77.4
K-LLaVABench	80.9	86.3	94.1	95.1
K-DTCBench	87.9	81.7	82.1	79.6

Korean Cultural Benchmark

Benchmark	InternVL3-14B	Ovis2-16B	Qwen2.5-VL-7B	VARCO-VISION-2.0-14B
K-Viscuit	71.7	77.0	70.9	72.9
PangeaBench (ko)	77.2	76.9	76.6	75.2

Text-only Benchmark

Benchmark	InternVL3-14B	Ovis2-16B	Qwen2.5-VL-7B	VARCO-VISION-2.0-14B
MMLU	78.5	78.4	4.6	77.7
MT-Bench	8.93	8.59	8.07	8.88
KMMLU	51.4	49.3	39.6	57.4
KoMT-Bench	7.01	7.91	6.84	7.95
LogicKor	7.00	7.94	6.55	7.86

Note: Some models show unusually low performance on the MMLU benchmark. This is primarily due to their failure to correctly follow the expected output format when only few-shot exemplars are provided in the prompts. Please take this into consideration when interpreting the results.

OCR Benchmark

Benchmark	PaddleOCR	VARCO-VISION-2.0-14B
CORD	91.4	93.3
ICDAR2013	92.0	93.2
ICDAR2015	73.7	82.7

Usage

To use this model, we recommend installing transformers version 4.53.1 or higher. While it may work with earlier versions, using 4.53.1 or above is strongly recommended, especially to ensure optimal performance for the multi-image feature.

The basic usage is identical to LLaVA-OneVision:

python

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

model_name = "NCSOFT/VARCO-VISION-2.0-14B"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    attn_implementation="sdpa",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_name)

conversation_1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
            {"type": "text", "text": "What is shown in this image?"},
            ],
    },
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": "There is a red stop sign in the image."},
            ],
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
            {"type": "text", "text": "What about this image? How many cats do you see?"},
            ],
    },
]
conversation_2 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
            {"type": "text", "text": "이 이미지에는 무엇이 보이나요?"},
            ],
    },
]

inputs = processor.apply_chat_template(
    [conversation_1, conversation_2],
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    padding=True,
    return_tensors="pt"
).to(model.device, torch.float16)

generate_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
outputs = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(outputs)

The following shows the input required for using OCR with text localization, along with the corresponding output: