Why Qwen is gaining popularity
Alongside the popularity of ChatGPT, Claude, and Gemini, interest in Qwen has grown noticeably. The reason isn't just model quality but also Alibaba's open approach to building out the ecosystem. Many versions can be run locally, integrated into your own projects, and used without being tied to a single service.
What is Qwen AI
Qwen is a lineup of large language models (LLMs) created by Alibaba Cloud. The project was publicly unveiled in 2023 under the name Tongyi Qianwen and later received the international name Qwen.
Today several directions are being developed under this brand at once:
- Qwen text models;
- Qwen Coder models for programming;
- multimodal models for working with images and video;
- solutions for building AI agents;
- tools for integration into apps and services.
In effect, Alibaba is building its own AI ecosystem that competes with OpenAI and Google as well as with other Chinese developers, including DeepSeek.
What Qwen can do
Modern Qwen models can handle most of the tasks people typically turn to neural networks for:
- answering questions;
- writing and editing text;
- translating material;
- analyzing documents;
- helping with learning;
- generating program code;
- working with images and multimedia content.
Alibaba pays particular attention to reasoning quality and handling complex multi-step tasks. Newer versions of the models are better at scenarios that require solving a problem step by step or carrying out a chain of actions.
Qwen 3.7 Plus: a new generation of models
The latest major update is the Qwen 3.7 Plus model. Alibaba positions it as a universal system for complex tasks involving reasoning, programming, and agentic scenarios.
According to the developers, the main focus during development was the quality of logical analysis, the ability to work with tools, and the ability to solve multi-step tasks.
The model is designed both for regular conversation with users and for more complex scenarios that require carrying out a sequence of actions to reach a result.
What is Qwen Coder
Qwen Coder is a specialized branch within the Qwen ecosystem focused on programming.
Models in this series are trained on large volumes of program code and are designed to solve development tasks.
Main use cases:
- generating code from a text description;
- autocompleting functions;
- finding and fixing bugs;
- refactoring;
- explaining someone else's code;
- working with repositories;
- building AI tools for development.
In essence, Qwen Coder is an analog of solutions like GitHub Copilot, Claude Code, and other AI assistants for programmers.
What makes Qwen3-Coder interesting
As part of expanding the lineup, Alibaba released Qwen3-Coder — a model geared toward more complex development tasks and an agentic approach to programming.
It can:
- write code from a technical specification;
- work with multiple files;
- use external tools;
- carry out multi-step development tasks.
The model is built for working with complex projects where a large amount of context needs to be taken into account and interaction with several parts of the codebase happens at once.
That's why Qwen Coder is increasingly being used as a full-fledged developer assistant.
How Qwen differs from other neural networks
There are plenty of strong language models on the market, but Qwen has a few distinguishing features.
The main one is the openness of a significant part of the ecosystem. Many models can be run locally, integrated into your own products, and adapted for specific tasks.
Thanks to this, Qwen is actively used not only by everyday users but also by developers, researchers, and companies that need control over their infrastructure and data.
How to start using Qwen
There are several ways to try Qwen.
The simplest option is to use the official Qwen Chat web interface through a browser. For running open models locally, popular tools like Ollama or LM Studio work well.
Developers can use Qwen Code and other command-line tools, as well as connect models via API and cloud services.
This approach lets you pick the format that fits the task at hand — from everyday conversation to building your own AI applications.
Practical uses of Qwen
For everyday users, Qwen can act as a universal AI assistant:
- searching for information;
- helping with learning;
- writing text;
- translating material;
- working with documents.
For businesses, the possibilities are broader:
- automating customer support;
- corporate chatbots;
- document analysis;
- building internal AI tools;
- developing AI agents.
Developers most often use Qwen Coder to speed up writing code and automate routine tasks. For example, the model can help make sense of an existing project, suggest a refactor, or generate tests for already-written functions.
Support for AI agents
One of Qwen's key areas of development remains AI agents — systems capable of carrying out an entire sequence of steps toward a goal instead of just a single action.
Rather than simply answering a request, an agent can analyze the task, call on tools, gather additional data, and make intermediate decisions.
This approach is in demand in programming, analytics, research, and enterprise automation, which is why Alibaba keeps investing more in the agentic capabilities of its models.
Limitations and quirks
Despite its rapid rise in popularity, Qwen can't be considered a universal replacement for every existing neural network.
A few things worth keeping in mind:
- response quality can vary between model versions;
- some features are only available through Alibaba's cloud infrastructure;
- part of the ecosystem's services is oriented primarily toward the Asian market;
- the model's output needs human review for important tasks.
Like other modern neural networks, Qwen can make mistakes, so critical information should always be double-checked.
Conclusion
Qwen has long since stopped being just a Chinese alternative to ChatGPT. Today it's a full-fledged AI ecosystem that includes language models, programming tools, multimodal solutions, and technology for building AI agents.
The Qwen Coder direction is especially interesting, helping automate development tasks and speed up work with code.
The development of Qwen 3.7 Plus shows that Alibaba continues to bet on complex reasoning, programming, and autonomous task execution. For users, this means another strong player entering the AI market; for developers, it means access to modern models that can be used both in the cloud and in their own projects.
Where to try other neural networks
GPTunneL brings together popular neural networks for working with text, programming, document analysis, image generation, and everyday tasks.
The platform offers ChatGPT, Claude, Gemini, DeepSeek, Qwen, and other modern models. It's convenient when you need to quickly compare results, pick the best answer, or find the right neural network for a specific task.
For developers, this approach makes it possible to test different AI models without setting up separate subscriptions or figuring out access configuration for each service. For everyday users, it's a simple way to get access to the most in-demand AI tools in a single interface.
If getting to know Qwen makes you want to compare its capabilities with other models, you can do that right in GPTunneL.