In January 2025, the Qwen team at Alibaba Cloud introduced Qwen 2.5 VL — a flagship multimodal model designed to handle text, images, and video. It marks a significant leap forward from previous versions, offering enhanced capabilities for visual perception and interaction with the world around it.
Qwen 2.5 VL stands out not just in scale, but in how it processes visual input. Unlike many other multimodal models, it can handle images of varying resolutions without the need for rigid preprocessing, which leads to more accurate interpretation of complex or non-standard visuals.
A core strength of the model is its ability to localize objects with high precision using bounding boxes or key points — it's not just "looking" at the image but identifying what matters. This makes it reliable for UI recognition, document structure parsing, and working with technical illustrations.
Qwen 2.5 VL also excels at document analysis. It can extract structured data from tables, diagrams, filled forms, and scanned invoices — no manual pre-parsing required. That makes it highly useful for back-office automation, legal processes, and enterprise-level workflows.
When it comes to video, the model can track and describe events in multi-hour recordings with second-level accuracy. This unlocks applications in video surveillance analytics, educational content, and video-based marketing.
And finally, Qwen 2.5 VL can act as an interactive visual agent. It interprets what’s happening on screen, recognizes UI elements, and follows visual prompts to take action, enabling automated workflows, UI testing, or assistants that operate through the visual layer, not APIs.
Below, we’ve collected some of the most practical and high-impact use cases where Qwen 2.5 VL has proven its value — from robotics to industrial monitoring.
You can try the model yourself via the is*smart subscription — no setup, no dependencies, and no delays. Everything is already deployed and optimized for production or testing.
Researchers developed NORA, a compact vision-language agent built on Qwen 2.5 VL-3B. Designed to improve robot-environment interaction under limited compute, it interprets visual scenes, understands commands, and generates step-by-step actions. The model uses the optimized FAST+ tokenizer and a training dataset of nearly one million real-world robot demos.
NORA is a great example of a multimodal LLM deployed in the real world — not just a lab demo. It’s suitable for mobile robots, logistics systems, or industrial operations where quick visual interpretation and decision-making are critical.
OmniAD introduces a new way to handle industrial anomalies by combining visual and textual reasoning in one system. Instead of merely detecting outliers, it explains why they occur. The framework uses Qwen 2.5 VL to generate visual masks and textual descriptions directly from images, skipping the need for manual thresholds.
To improve performance in low-data environments, the team combined supervised fine-tuning with reinforcement learning, using multiple reward functions. The result? OmniAD achieved a benchmark score of 79.1 on MMAD — outperforming even GPT-4o and raw Qwen 2.5 VL-7B. It's a powerful case for models that don't just "see" but reason.
Another study compared various AI agent architectures — from classic vision-language pipelines to Qwen 2.5 VL–based models. In scenarios requiring the model to match visual inputs with instructions and generate action sequences, Qwen 2.5 VL stood out with fast, stable outputs.
The researchers noted that multimodal agents are better suited to applied tasks than abstract logical reasoning. In areas where predictability, speed, and resource efficiency are vital, Qwen 2.5 VL proves highly competitive — a solid foundation for real-world integration.
Qwen 2.5 VL makes sense if:
Not every task needs multimodality.
DeepSeek R1 is built for technical reasoning and code-heavy workflows. If your main focus is human-facing content — UX copy, help articles, chatbot responses — a smaller, language-first model like Gemma 3 may be a better fit.
For tasks that involve both text and visual input, Qwen 2.5 VL remains one of the most capable and production-ready models available.
It’s simple: Qwen 2.5 VL is available via the is*smart subscription. Sign up, and you can start using it for multimodal tasks — no complex setup, no need to manage compute, no reliance on third-party APIs.