Model Overview

Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters, with only 5.5B parameters activated per token, and supports a context window of up to 256K tokens.

The architecture of Ling-3.0-flash-VL is designed to integrate visual information into real-world reasoning and agentic workflows.

• A ViT visual encoder extracts features from images and videos, while a two-layer MLP projector aligns visual features with text representations for unified multimodal understanding and reasoning;
• VideoRoPE encodes both spatial positions and temporal order, enabling the model to understand visual changes over time and supporting tasks such as event localization, long-video question answering, and video clip editing;
• A 42-layer hybrid backbone alternates KDA and Gated MLA layers at a 5:1 ratio, enabling efficient long-context processing across text, images, videos, and extended agent task histories;
• A sparse MoE architecture maintains a total model capacity of 124B parameters while activating only 5.5B parameters per token, balancing strong multimodal capabilities with inference efficiency.

Overall, these designs make vision more than just an input, integrating it into the complete process of understanding, reasoning, planning, acting, and verification.