Qwen2-VL: Alibaba Releases Latest Vision-Language Model with Extended Video Understanding
Alibaba's Qwen team has released Qwen2-VL, the latest iteration of their vision-language model series built on the Qwen2 foundation. The model claims state-of-the-art performance on visual understanding benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA. A notable capability is understanding videos exceeding 20 minutes in length for question answering, dialog, and content creation tasks.
Related guides (3)
Related events (8)
Qwen2.5-VL: Alibaba's New Flagship Vision-Language Model Released in 3B/7B/72B Sizes
Alibaba's Qwen team has released Qwen2.5-VL, their new flagship vision-language model, representing a significant upgrade over Qwen2-VL. The release includes both base and instruct variants in three sizes (3B, 7B, 72B), all open-weighted and available on Hugging Face and ModelScope. The 72B instruct model is also accessible via Qwen Chat. Key capabilities highlighted include enhanced visual understanding, with the model positioned as a major step forward in multimodal performance.
Qwen2.5-VL-32B: Reinforcement-Learning-Optimized Vision-Language Model Released
Alibaba's Qwen team has released Qwen2.5-VL-32B-Instruct, a 32-billion-parameter vision-language model built on the Qwen2.5-VL series and further optimized with reinforcement learning. The model is open-sourced under the Apache 2.0 license and available on Hugging Face and ModelScope. It follows the January 2025 launch of the broader Qwen2.5-VL series, positioning the 32B scale as a balance between capability and deployment practicality.
Introducing Qwen-VL-Plus and Qwen-VL-Max: Upgraded Multimodal Models from Alibaba
Alibaba's Qwen team has launched two enhanced versions of their multimodal model, Qwen-VL-Plus and Qwen-VL-Max, building on the open-sourced Qwen-VL released in September 2023. Key improvements include substantially boosted image reasoning capabilities, enhanced detail recognition and text extraction from images, and support for high-definition images exceeding one million pixels across various aspect ratios. The upgrades represent a significant step forward in the Qwen-VL series' generalization and visual understanding capabilities.
Qwen VLo: Unified Multimodal Understanding and Generation Model
Alibaba's Qwen team has announced Qwen VLo, a new model that unifies multimodal understanding and image generation in a single architecture. Building on the Qwen2.5 VL lineage, the model is positioned to both comprehend and generate high-quality visual content. This represents a step toward unified perception-and-creation models, a direction several frontier labs are pursuing simultaneously.
QVQ-72B-Preview: Qwen Visual Reasoning Model Release
Alibaba's Qwen team has released QVQ-72B-Preview, a 72-billion parameter multimodal model designed to integrate visual understanding with advanced reasoning capabilities. The model is positioned as an extension of Qwen's language reasoning work into the visual domain. It is available on GitHub, Hugging Face, ModelScope, and Kaggle with a live demo.
Qwen2-Audio: Multimodal Audio-Language Model Release
Alibaba's Qwen team releases Qwen2-Audio, the successor to Qwen-Audio, capable of accepting both audio and text inputs and generating text outputs. The model is positioned as a step toward AGI by extending large language model capabilities to audio modalities. It is released with accompanying paper, GitHub repository, and model weights on Hugging Face and ModelScope.
QVQ-Max: Alibaba Qwen Releases Visual Reasoning Model with Multimodal Chain-of-Thought
Alibaba's Qwen team has officially released QVQ-Max, a visual reasoning model succeeding the December 2024 QVQ-72B-Preview. The model is designed to analyze and reason over images and videos, covering domains including mathematics, programming, and creative tasks. It represents a step beyond the exploratory preview, positioning as a production-grade multimodal reasoning system.
Qwen-VLA: Unified Vision-Language-Action Model Across Robot Tasks, Environments, and Embodiments
Alibaba's Qwen team presents Qwen-VLA, a unified embodied foundation model that extends the Qwen vision-language stack to continuous action and trajectory generation via a DiT-based action decoder. The model is jointly pretrained on diverse data spanning manipulation trajectories, egocentric demonstrations, synthetic simulation, and navigation data, with embodiment-aware prompt conditioning to support multiple robot platforms. A unified action-and-trajectory prediction framework covers manipulation, navigation, and trajectory prediction tasks. Benchmarks show strong results: 97.9% on LIBERO, 73.7% on Simpler-WidowX, 69.0% OSR on R2R navigation, and 76.9% average OOD success in real-world ALOHA experiments.