Qwen2-VL-72B-Instruct
A multimodal neural network from Alibaba with 73.4 billion parameters for understanding images and videos.
Overview
Qwen2-VL-72B-Instruct
Description of the Qwen2-VL-72B-Instruct neural network
General information about the model
Qwen2-VL-72B-Instruct is a multimodal neural network developed by Alibaba. The model contains 73.4 billion parameters and is designed for comprehensive processing of visual information: images and videos. It was announced in late August 2024 and is one of the advanced solutions in the field of computer vision and multimodal analysis.
Key technological features
The model architecture is built on dynamic resolution, which allows processing images of varying quality and size without losing accuracy. In addition, improved positional embeddings (M-ROPE) are used, which enhance the quality of understanding spatial relationships between objects in an image. The model supports both visual perception and text-based reasoning, opening up opportunities for solving a wide range of tasks.
Scope of application
The neural network can perform step-by-step reasoning based on visual content, recognize text in images across different languages, and interact with video data in an agent-based mode. This makes it useful in both research and applied scenarios.
Specifications of Qwen2-VL-72B-Instruct
| Specification | Value |
|---|---|
| Developer | Alibaba |
| Type | Multimodal model |
| Parameters | 73.4 billion (73.4B) |
| Release date | August 29, 2024 |
| Average score | 75.8% |
| License | tongyi_qianwen |
| Knowledge cutoff | June 30, 2023 |
Who is the Qwen2-VL-72B-Instruct neural network suitable for?
AI researchers
The model is of interest to researchers and engineers working on computer vision, multimodal learning, and visual reasoning tasks. Thanks to its open license and large number of parameters, the neural network can be used in research projects and experiments.
AI product developers
Qwen2-VL-72B-Instruct is suitable for specialists building applications based on multimodal analysis. The ability to process images and videos, as well as the availability of an API, make the model convenient for integration into commercial products.
Data specialists
Analysts and data scientists who extract information from visual content, recognize text in images, or analyze video materials can use this model as a powerful tool for domain-specific tasks.
How to use the Qwen2-VL-72B-Instruct neural network?
Access via API
One of the main ways to work with the model is through the API. This allows connecting the neural network to applications and services without deploying your own infrastructure.
Local deployment
Thanks to its open-source status, the model can be deployed on your own servers. However, due to the large number of parameters (73.4 billion), significant hardware with sufficient GPU memory is required to run it.
Integration into research projects
Developers can download the model and use it in their research pipelines, fine-tune it for specific tasks, or test it on their own datasets.
Key features of Qwen2-VL-72B-Instruct
Image and video support
The model can accept both static images and video sequences as input. This is a key advantage over models that work with only one type of data.
Dynamic resolution and improved embeddings
Images are processed with adaptation to their original resolution, which helps avoid loss of detail. Improved M-ROPE positional embeddings provide more accurate understanding of the spatial arrangement of objects.
Step-by-step reasoning and multilingual text recognition
The neural network can carry out logical reasoning chains based on visual content. In addition, it recognizes text in images across different languages, which is useful for OCR and document analysis tasks.
Agent-based interaction with video
The model supports scenarios in which it acts as an agent capable of analyzing a video stream and making decisions based on visual information in real time.
Advantages of Qwen2-VL-72B-Instruct
High multimodality
The model combines image, video, and text processing in a single architecture, simplifying the creation of comprehensive solutions and reducing the need to use several different models.
Open license
The tongyi_qianwen license allows the model to be freely used and modified in research and commercial projects, which is important for the developer community.
Modern architecture
The use of dynamic resolution and M-ROPE embeddings ensures high-quality visual understanding, as confirmed by an average score of 75.8%.
Disadvantages of Qwen2-VL-72B-Instruct
High resource requirements
Working with the model requires powerful hardware. 73.4 billion parameters require a significant amount of GPU memory, which may be unavailable to small teams or individual developers.
Limited knowledge cutoff
The model is trained on data current as of June 30, 2023. This means that information about events that occurred after that date may be incomplete or absent.
Release date and maturity
The model was released in August 2024, so the ecosystem of tools, documentation, and ready-made solutions around it may be less developed compared with more mature alternatives.
What tasks does Qwen2-VL-72B-Instruct solve?
Solving complex tasks with visual context
The model can analyze images and videos, identify relationships between objects, and formulate logical conclusions. This is in demand in robotics, security systems, and automated quality control.
Multilingual text recognition in images
Qwen2-VL-72B-Instruct is suitable for extracting text information from photographs, documents, signs, and other visual media in different languages.
Agent-based interaction based on video
The model can be used in scenarios that require autonomous video stream analysis, such as video surveillance, drone control, or human-machine interfaces.
Qwen2-VL-72B-Instruct pricing
At the moment, information about specific prices for using the model has not been disclosed in official sources. The cost of using the model via API and the terms of commercial licensing should be clarified on the developer's website — Alibaba. For local deployment, costs consist of hardware and electricity expenses.
Terms of use for Qwen2-VL-72B-Instruct
The model is distributed under the tongyi_qianwen license. This is an open license that allows using the neural network for scientific and commercial purposes. Detailed terms of use, including possible restrictions and attribution requirements, should be reviewed in the text of the license itself, published on Alibaba's official resources.
Availability of Qwen2-VL-72B-Instruct
The model is available for download and integration via API. Since the neural network belongs to the open-source category, the source code and model weights are publicly available. The exact distribution method (repository, model platform) is listed as "unknown" in the source data, so it is recommended to refer to the official Alibaba Cloud and Qwen channels for up-to-date links.
How Qwen2-VL-72B-Instruct differs from alternatives
Comparison with other multimodal models
Qwen2-VL-72B-Instruct stands out among alternatives because it simultaneously supports image and video processing, uses dynamic resolution, and improved positional embeddings. Many competing models, such as Qwen2.5 VL 72B Instruct, Qwen2.5 VL 32B Instruct, or QvQ-72B-Preview, have similar functionality but differ in architecture version or number of parameters.
Differences in architecture and functions
Unlike lighter versions such as Qwen2.5 VL 7B Instruct, the model with 73.4 billion parameters can solve more complex tasks thanks to its larger body of knowledge and better reasoning ability. Qwen2-VL-72B-Instruct also differs from pure text models (for example, Qwen3.5-397B-A17B) in having full multimodal processing.
Position in the Qwen lineup
The model is part of the Qwen2-VL family and occupies an intermediate position between earlier and newer versions. For example, Qwen3 VL 32B Thinking and Qwen2.5-Omni-7B are later or more specialized developments.
Conclusion
Qwen2-VL-72B-Instruct is a powerful multimodal neural network from Alibaba with 73.4 billion parameters that can process images and videos. It uses dynamic resolution and improved positional embeddings for visual understanding, step-by-step reasoning, multilingual text recognition, and agent-based interaction. The model was announced in late August 2024, is distributed under an open license, and is suitable for both research and applied tasks in the field of computer vision.