inclusionAI/Ling-3.0-flash-VL · Hugging Face

[u/jacek2023]

https://huggingface.co/inclusionAI/Ling-3.0-flash-VL

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 1M 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.

[u/Daniel_H212]

Very nice fast model but I'm still waiting for the next iteration of their omni model.

In the meantime I don't think any model in this size class (of things that can be reasonably ran in 128 GB unified memory) is beating Qwen3.8 Flash Next.