Jina-VLM: Small Multilingual Vision Language Model

We present jina-vlm, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. The model achieves leading results on standard VQA benchmarks and multilingual evaluations while preserving competitive text-only performance. Model weights and code are publicly released at https://huggingface.co/jinaai/jina-vlm. Vision-language models (VLMs) combine pretrained vision encoders with large language models to tackle tasks requiring joint visual and textual understanding (Alayrac et al., 2022; Liu et al., 2023). Recent VLMs have achieved strong results on visual question answering (VQA), OCR, and multimodal reasoning.