Qwen3-VL is here!

Описание: Key Highlights:

Visual Agent Capabilities: Qwen3-VL can operate computer and mobile interfaces — recognize GUI elements, understand button functions, call tools, and complete tasks. It achieves top global performance on benchmarks like OS World, and using tools significantly improves its performance on fine-grained perception tasks.

Superior Pure-text Performance: Qwen3-VL employs early-stage joint pretraining of text and visual modalities, continuously strengthening its language capabilities. It ultimately achieves pure-text task performance comparable to Qwen3-235B-A22B-2507 — the flagship pure-text model — making it a truly “text-grounded, multimodal powerhouse” for the next generation of vision-language models.

Greatly Improved Visual Coding: It can now generate code from images or videos — for example, turning a design mockup into Draw.io, HTML, CSS, or JavaScript code — making “what you see is what you get” visual programming a reality.

Much Better Spatial Understanding: 2D grounding is strengthened and the coordinate is transformed from absolute to relative, enabling more robust estimation of object locations, viewpoint (camera pose) changes, and occlusion structure. The model also supports 3D grounding, providing a foundation for complex spatial reasoning and embodied AI applications.

Long Context & Long Video Understanding: Natively support 256K tokens of context, expandable up to 1 million tokens. This means you can input hundreds of pages of technical documentation, entire textbooks, and even two‑hour videos of meetings or lectures — and achieves reliable long‑context retention and precise retrieval, including second‑level timestamp localization in video.

Stronger Multimodal Reasoning (Thinking Version): The Thinking model is optimized for STEM reasoning. On complex problems, it attends to fine‑grained cues, performs step‑by‑step decomposition, analyzes causal dependencies, and produces logically consistent, evidence‑grounded solutions. It achieves state‑of‑the‑art results on benchmarks including MathVision, MMMU, and MathVista.

Upgraded Visual Perception & Recognition: Improvements to the quality and diversity of the pretraining corpus have expanded the model’s recognition ability to a broader range of objects and entities, including celebrities, anime characters, consumer products, landmarks, and flora/fauna, — covering both everyday life and professional “recognize anything” needs.

Better OCR Across More Languages & Complex Scenes: OCR now supports 32 languages (up from 10), covering more countries and regions. It demonstrates greater robustness under challenging real-world conditions like poor lighting, blur, or tilted text. Recognition accuracy for rare characters, ancient scripts, and technical terms has also improved significantly. Its ability to understand long documents and reconstruct fine structures is further enhanced.