Alibaba Qwen AI is a general artificial intelligence model that supports multimodal task processing and provides intelligent solutions.
The purpose of Qwen AI in China
Qwen AI is a general artificial intelligence big model launched by Alibaba Group, which supports multimodal task processing and cross-industry scenario applications.
It has a wide range of uses, including but not limited to:
- Text generation: writing articles, summaries, advertising copy and other content.
- Code writing: providing code snippet or complete code generation services.
- Data analysis: quickly extract key information from data to assist enterprise decision-making.
- Image understanding: analyzing image content, supporting visual question answering, document parsing and other tasks.
- Intelligent customer service: quickly respond to customer needs and improve service efficiency.
- Intelligent recommendation: accurately recommend content and enhance user experience.
- Medical health: assist medical image analysis and disease diagnosis, and provide accurate support.
Qwen AI development company
Qwen AI was developed by Alibaba Group and is a generative AI model based on Transformer architecture and ultra-large-scale parameter training.
Its design goal is to provide enterprises and developers with efficient and flexible artificial intelligence tools.
Qwen AI’s full English name
Qwen AI’s full English name is Qwen, which means “general meaning, thousands of questions”, and is the name of the general artificial intelligence model launched by Alibaba Group.
Qwen AI’s features
Qwen AI has the following significant features:
- Multimodal task processing: Supports multiple input forms such as text, images, audio, and video to achieve cross-modal understanding and generation.
- Mixed expert architecture: The mixed expert (MoE) architecture is used to improve reasoning efficiency and reduce computing resource consumption.
- Long text processing: Supports context windows of up to 1 million tokens to improve processing efficiency.
- Open source support: Provides open source versions (such as Qwen-72B), allowing researchers to conduct secondary development and tuning under the premise of compliance.
- Multi-language support: Supports multiple languages such as Chinese, English, Japanese, Korean, and Arabic.
Qwen AI’s technical foundation
Qwen AI is based on the Transformer architecture and uses the mixed expert (MoE) architecture for design and training. Its pre-training data scale reaches 20 trillion tokens. Through large-scale text data pre-training, it supports multimodal task processing and cross-industry scenario applications.
Qwen AI model type
Qwen AI is a generative AI model that supports multimodal task processing. Its model types include basic language models, chat models, and models for specific fields and tasks (such as Code-Qwen for programming and Math-Qwen for mathematics).
Qwen AI supported languages
Qwen AI supports multiple languages, including Chinese, English, Japanese, Korean, Arabic, etc., to meet the needs of global applications.
Qwen AI’s main commercial applications
Qwen AI has a wide range of applications in the commercial field, including but not limited to:
- Intelligent customer service: Provide efficient question-answering and support services to enhance customer experience.
- Content generation: Used to generate articles, summaries, advertising copy and other content, suitable for content creators.
- Educational assistance: Provide personalized learning suggestions and homework correction services to help students better master knowledge.
- Programming assistance: Generate code snippets based on user needs and provide programming-related help.
- Industry applications: Provide customized solutions in professional fields such as finance, medical care, and law to meet specific industry needs.
Qwen AI is currently used in a wide range of fields
Qwen AI is currently used in a wide range of fields, including but not limited to:
- Education: Intelligent teaching assistants, personalized learning support, popular science education, etc.
- Finance: Intelligent customer service, risk assessment, market forecasting, etc.
- Medical: Auxiliary diagnostic tools, medical support, etc.
- Entertainment: Chatbots, interactive entertainment dialogues, etc.
- Smart home: Connect with smart home, car systems, etc. to achieve voice control and remote monitoring.
The latest version of Qwen AI
The latest version of Qwen AI is Qwen 2.5-Max, which performs well in multiple benchmarks and successfully surpasses models such as DeepSeek-V3, which is in the limelight.
Qwen AI development history
Qwen AI was launched by Alibaba Group. After several version iterations, it has become a powerful generative AI model. Its development history includes several important nodes:
- The launch of the first large-scale language model Qwen-7B, marking the launch of its open source project.
- The release of Qwen 2.5-Max version, supporting multimodal task processing and cross-industry scenario applications.
Qwen AI vs. ChatGPT comparison
| Dimension | Qwen AI | ChatGPT |
| Development company | Alibaba Group | OpenAI |
| R&D Country | China | USA |
| Technical foundation | Based on Transformer architecture, adopts Mixed Expert (MoE) architecture | Based on GPT architecture |
| Multimodal support | Supports multiple input forms such as text, image, audio, video, etc. | Mainly supports text input |
| Open source support | Provides open source version (such as Qwen-72B) | No open source version is provided |
| Language support | Supports multiple languages such as Chinese, English, Japanese, Korean, Arabic, etc. | Mainly supports English |
| Application scenarios | Widely used in education, finance, medical care, intelligent customer service, intelligent recommendation and other fields | Widely used in natural language processing, text generation and other fields |
| Payment model | Provides API call-based services, and the specific price varies depending on the scale of use and licensing model | Free trial and paid subscription model are available |
| Advantages and disadvantages | Advantages: multi-modal support, open source support, multi-language support; Disadvantages: performance in some areas may not be as good as ChatGPT | Advantages: powerful natural language processing capabilities; Disadvantages: mainly supports English, no open source version is provided |
Qwen AI has problems with security and privacy
Qwen AI has problems with security and privacy, generally including data leakage, insufficient privacy protection, model attacks, etc.
The following is my detailed introduction to the problems of Qwen AI in security and privacy:
1. Data leakage risk:
Qwen AI may have data leakage problems during training, such as overlap between training and test sets, data label leakage, feature leakage, etc. These problems may cause the model to bias or mispredict unseen data, thereby leaking user privacy.
When conducting security assessments, the open source version of Qwen AI (such as Qwen-72B) showed deficiencies in certain detection methods, such as psychological suggestion attacks, prefix induction, and prefix injection, which may cause the model to generate inappropriate or harmful answers, thereby leaking sensitive information.
2. Insufficient privacy protection:
Although Qwen AI provides some privacy setting options when processing user data, such as dynamic visible range, message sending permissions, etc., users still need to handle friend requests with caution to avoid personal information leakage.
Although Qwen AI’s private chat function uses advanced encryption technology, it may still be at risk of privacy leakage when facing certain attack methods.
3. Model attack risk:
As an open source model, Qwen AI may face the risk of being attacked. Attackers may induce the model to generate inappropriate or harmful answers through malicious input, exploiting bias and harmful content in pre-training data, etc.
When dealing with certain offensive questions, Qwen AI may not be able to effectively identify and refuse to answer, thereby leaking sensitive information or spreading inappropriate content.
4. Security audit and supervision:
Qwen AI still has shortcomings in audit and supervision. Although Qwen AI has been applied in audit practice, overall, its measures in security audit and supervision still need to be strengthened.
Relevant departments have issued a series of guiding opinions, but Qwen AI still needs to continuously improve its security audit and supervision mechanism in specific implementation.
5. Security and privacy comparison with ChatGPT:
Compared with ChatGPT, Qwen AI has a higher proportion of Chinese in training data, but it may have shortcomings in international language support and privacy protection.
ChatGPT may be overly conservative in medical advice due to European and American privacy laws, and Qwen AI may cause certain problems in training data bias, such as a decline in the pass rate of female resumes in recruitment companies.
These information reflect that Qwen AI has certain problems in security and privacy. However, as China’s leading artificial intelligence product, Qwen AI is also constantly training data to solve various existing problems. I believe that China’s Qwen AI will definitely be better in the future.
China’s Qwen AI in the next 30 years
In the next 30 years, Qwen AI is expected to make further progress in the following aspects:
- Technology upgrade: Deepen multimodal fusion, improve video generation, 3D reconstruction and other capabilities; explore AGI (general artificial intelligence) paths, and enhance cross-domain adaptive capabilities.
- Application scenario expansion: More widely used in smart manufacturing, smart cities and other fields; build an open technology ecosystem with global partners.
- Global layout: Actively expand overseas markets and enhance Qwen AI’s competitiveness in the international market.
- Safety and ethics: Strengthen data security protection, prevent content generation bias, and ensure compliance of technology applications.
With the continuous advancement of technology and the expansion of application scenarios, Qwen AI is expected to become a leader in the field of artificial intelligence in China and even the world, bringing a better future to human society.
Qwen AI Latest News
Qwen AI has made significant progress in both technology and application recently. In terms of technology, the Qwen2.5-VL visual-language model was released, which significantly improved visual understanding and reasoning capabilities, especially in mathematics, document understanding and video analysis. At the same time, Qwen AI proposed an innovative framework SeeGround in the field of 3D visual positioning, which can achieve accurate positioning without 3D training data, providing efficient solutions for augmented reality and robot navigation. The open source model family continues to expand, and the number of derivative models has exceeded 100,000, ranking first in the world’s largest open source model.
At the application level, Qwen AI has reached an exclusive cooperation with the 2025 Spring Festival Gala, becoming an e-commerce interactive platform and AI partner, and plans to launch an independent APP for the C-end.
In addition, Alibaba announced that it will invest more than 380 billion yuan in the next three years to build cloud and AI infrastructure to support the development of Qwen AI. Qwen AI has connected with 300,000 corporate customers, including China FAW and Ctrip, and its application scenarios cover multiple fields such as intelligent customer service, medical health, code generation, etc.
Alibaba Cloud releases multimodal AI model Qwen2.5-Omni-7B
On March 27, 2025, Alibaba Cloud released the first end-to-end full-modal large model Qwen2.5-Omni-7B of the Tongyi family. The model is designed for full-modal perception and can process multimodal inputs such as text, images, audio, and video, and generate text and natural language responses in real time.
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Qwen AI FAQs
Here are some frequently asked questions and answers about Qwen AI:
Qwen AI is a general artificial intelligence model launched by Alibaba Group, with the Chinese name “通义千问“. The model uses natural language as its core capability, supports multimodal task processing and cross-industry scenario applications, and aims to provide intelligent solutions through large-scale pre-training technology.
Qwen AI can be used for a variety of tasks, including but not limited to:
Text generation: writing articles, summaries, advertising copy, etc.
Code writing: providing code snippets or complete code generation services.
Data analysis: quickly extract key information from data to assist corporate decision-making.
Image understanding: analyzing image content, supporting visual question answering, document parsing and other tasks.
Intelligent customer service: quickly respond to customer needs and improve service efficiency.
Intelligent recommendation: accurately recommend content and enhance user experience.
Healthcare: assisting medical image analysis and disease diagnosis, providing accurate support.
Qwen AI has the following notable features:
Multimodal task processing: supports multiple input forms such as text, images, audio, and video to achieve cross-modal understanding and generation.
Hybrid expert architecture: uses a hybrid expert (MoE) architecture to improve reasoning efficiency and reduce computing resource consumption.
Long text processing: supports context windows of up to 1 million tokens to improve processing efficiency.
Open source support: provides open source versions (such as Qwen-72B), allowing researchers to conduct secondary development and tuning under the premise of compliance.
Multi-language support: supports multiple languages such as Chinese, English, Japanese, Korean, and Arabic.
Qwen AI has potential risks in terms of security, such as data leakage, insufficient privacy protection, and model attacks. It is recommended that users pay attention to privacy settings and model configuration when using it to avoid sensitive information leakage. At the same time, Qwen AI is also constantly improving security, such as optimizing the accuracy and logic of generated content through reinforcement learning to reduce the generation of inappropriate or harmful information.
Qwen AI has the following advantages over ChatGPT:
Chinese accounts for a higher proportion of training data: Chinese accounts for 65% of Qwen AI’s training data, while ChatGPT only accounts for 12%, which makes it superior in understanding Chinese culture.
Support for multimodal task processing: Qwen AI supports multiple input forms such as text, images, audio, and video, while ChatGPT mainly supports text input.
Open source support: Qwen AI provides an open source version, allowing researchers to conduct secondary development and tuning under the premise of compliance, while ChatGPT does not provide an open source version.
Users can quickly access Qwen AI through Alibaba Cloud or ModelScope platform, and fine-tune and optimize tasks according to their needs. At the same time, Qwen AI also provides detailed documentation and tutorials to help users get started quickly.
Yes, Qwen AI supports multiple languages such as Chinese, English, Japanese, Korean, Arabic, etc. to meet the needs of global applications.
The latest version of Qwen AI is Qwen 2.5-Max. This version performs well in multiple benchmarks and successfully surpasses the popular DeepSeek-V3 and other models.
Qwen AI has a wide range of application cases in education, finance, medical care, entertainment, smart home and other fields. For example:
Education: intelligent teaching assistants, personalized learning support, popular science education, etc.
Finance: intelligent customer service, risk assessment, market forecasting, etc.
Medical: auxiliary diagnostic tools, medical support, etc.
Entertainment: chatbots, interactive entertainment dialogues, etc.
Smart home: docking with smart home, car systems, etc. to achieve voice control and remote monitoring.
If you encounter environment configuration problems, users can try the following methods to solve them:
Check the Python version: Make sure you are using Python 3.7 or higher.
Install dependency packages: Use the pip install -r requirements.txt command to install the dependency packages required for the project.
Use virtual environment: It is recommended to run the project in a virtual environment to avoid dependency conflicts with other projects. You can use virtualenv or conda to create a virtual environment.
View logs and debug: Check the log output during the run process and look for abnormal information or warnings. Use Python’s pdb or other debugging tools to step through the code and observe the status of variables.
