Youtu-VL is a 4B-parameter Vision-Language Model that excels in both vision-centric and general multimodal tasks without needing task-specific modules. It uses a unique autoregressive supervision method to enhance visual understanding and preserve detailed information. The model supports various applications, from image classification to visual question answering.

OmniSVG is a unified framework for generating high-quality scalable vector graphics (SVG) using pre-trained Vision-Language Models (VLMs), which decouples structural logic from low-level geometry. It introduces the MMSVG-2M dataset with two million annotated SVG assets and supports multiple generation modalities, demonstrating superior performance over existing methods for diverse creative tasks. The model is designed to handle complexity ranging from simple icons to intricate illustrations, offering flexibility for professional design workflows.

Kimi-VL is an open-source Mixture-of-Experts vision-language model that excels in multimodal reasoning and long-context understanding with only 2.8B activated parameters. It demonstrates superior performance in various tasks such as multi-turn interactions, video comprehension, and mathematical reasoning, competing effectively with larger models while maintaining efficiency. The latest variant, Kimi-VL-A3B-Thinking-2506, enhances reasoning and visual perception capabilities, achieving state-of-the-art results in several benchmarks.