Qwen - What Is It and What Sets Alibaba's Chinese AI Model Apart?

Qwen is a Chinese AI model developed by Alibaba that, in just over a year, has evolved from being seen as a "Chinese alternative" into a genuine competitor to GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash - in benchmarks covering video understanding, document analysis, and mathematical reasoning, the Qwen 2.5-VL version outperforms these Western models. Alibaba's model is available for free via a browser (Qwen Chat at chat.qwen.ai) as well as locally, e.g., via Ollama, across many parameter configurations - from lightweight options like Qwen 3.5 9B to flagship Plus and Max versions with a context window reaching up to one million tokens.

If you are asking yourself "What is Qwen?" and whether it is worth treating as a real productivity tool rather than just a chatbot, the answer is yes - Qwen AI supports conversations, text generation, image and video generation, web search, code writing, and autonomous actions on computers and phones (launching apps, booking flights).

What is Qwen and who is behind this Chinese AI model

Qwen AI is a family of artificial intelligence models created by Alibaba - a direct competitor to American systems from OpenAI and Google. The technology is developed by Alibaba Cloud, the cloud services division of Alibaba Group, one of the world's largest tech conglomerates, previously known primarily for its e-commerce platforms (Alibaba.com, Taobao) and enterprise cloud computing infrastructure in Asia.

Formally, "Qwen" is the international trade name for the model, which operates in the Chinese market under the name Tongyi Qianwen (通义千问). This Chinese name has practical significance - under it, the model functions as a local chatbot available to users in China. Meanwhile, Alibaba uses the "Qwen" branding in global communications, technical documentation, and on platforms like Hugging Face and GitHub, where it releases open-source versions of its models.

Qwen was developed as part of a broader strategy by Chinese tech giants aiming to build an independent foundation of AI expertise - alongside DeepSeek and Baidu Ernie. Alibaba is developing it as a family rather than a single product: successive generations (Qwen2.5, Qwen3, Qwen3.5, Qwen3.6, Qwen3.7 Plus/Max) are released at regular intervals. This sets the project apart from one-off releases and signals the conglomerate's long-term, strategic investment in a world-class Chinese LLM.

Qwen's market position is driven by two factors. The first is the scale of Alibaba Cloud's computing resources, which allows it to train models with hundreds of billions of parameters. The second is its partial open-source policy - thanks to this, the model has reached a broad ecosystem of developers building their own implementations based on it, as detailed later in the sections covering licensing and local installations.

Evolution and technical specifications: from Qwen 2.5 to the revolutionary Qwen3 lineup

The Qwen model family has undergone a transformation: from efficient open-source systems to the multilingual Qwen3 lineup, trained on massive datasets and ranked among frontier AI models. The change involves not only parameter counts, but the computing architecture itself - transitioning from a dense model, which processes every query using the entire network, to a modular system where only a subset of components is activated.

Mixture of Experts (MoE) Architecture in Qwen Models

A key technical shift in the Qwen3 lineup is the Mixture of Experts (MoE) architecture: the model is divided into multiple specialized "experts," and a router activates only a small subset of them for each query. An example is the Qwen3-235B-A22B variant - it has 235 billion total parameters, but actively uses only about 22 billion of them to process a single token. This differs from classic dense models, such as some Llama variants, where every query engages all parameters of the network. MoE allows Alibaba to maintain a massive model knowledge capacity at a relatively lower computational inference cost. The efficiency of this architecture is verified by standard academic tests, including MMLU, which covers questions across dozens of fields of knowledge - one of the benchmarks cited by Alibaba when presenting the performance of Qwen3 models against the competition.

The Breakthrough Qwen3 Lineup and Its Massive Datasets

Qwen3, launched on April 28, 2025, introduced hybrid reasoning models: a single model operates in a fast-response mode or switches to a longer, multi-step reasoning process when the task requires it. The model supports 119 languages - a significant leap over previous generations

A New Era of AI Agents: Characteristics of Qwen3.7 Plus and Qwen3.7 Max Models

Qwen3.7, released in May 2026, is divided into two variants tailored to different types of AI agent work: Qwen3.7 Plus (a multimodal agent operating on text and images) and Qwen3.7 Max (a closed-weight reasoning-agent model focused on pure text and deep inference). This division replaces the earlier scheme from Qwen 2.5 Max and Qwen 2.5 Plus, where both variants differed mainly in parameter scale rather than agentic function.

Qwen3.7 Plus combines language and image understanding with deep reasoning, tool calling, and autonomous iteration. The model independently plans a sequence of steps, checks intermediate results, and corrects its own actions without requiring user intervention at every turn. These three elements - multimodality, external tool calling, and iterative response refinement - practically define agentic capabilities: the model does not just answer a question, but executes a multi-step task to completion. A very large operational context allows the agent to retain long chains of actions, project documentation, or the history of a multi-hour work session in memory without losing earlier conclusions.

Qwen3.7 Max takes a different direction - it is a model optimized for reasoning and agentic tasks, with a context window reaching approximately 1,000,000 tokens. Such capacity allows it to process extensive codebases, entire code repositories, or multi-volume reports at once without chunking the input data. Its closed-weight status sets Max apart from the open variants of the Qwen family described later in the article: Alibaba provides access to it primarily via API and cloud platforms, not for local installation.

The practical significance of this architecture goes beyond chatting with a bot. An autonomous agent capable of performing tasks for hours - bookings, research, report generation with external tool calls - changes how companies design automation, including processes related to AI SEO, where an agent can autonomously analyze data and implement fixes. However, the scale of context and agentic capabilities do not eliminate the risk of hallucinations: longer chains of autonomous reasoning can still lead to flawed intermediate conclusions, which the model carries over into subsequent task steps if the verification mechanism fails to catch the error early in the iteration.

Multimodality in Practice: Capabilities of Qwen 2.5-VL and Qwen2.5-Omni-7B

Qwen 2.5 models deliver multimodality on two distinct levels: Qwen 2.5-VL processes text, images, and video in parallel as a vision-language agent, while Qwen2.5-Omni-7B adds an audio channel to this, generating voice in response to voice in real time.

Qwen 2.5-VL as an Autonomous Computer Agent

Qwen 2.5-VL, released in January 2025, combines text and image analysis with video understanding: it recognizes sequences of events in footage and accurately counts elements visible in an image. The ability to take control of computers and phones - such as launching apps on its own or booking flights - was already announced during the model's presentation. However, full agentic features with autonomous device control truly developed in the newer Qwen3.x lineups, including Qwen3.7 Plus described in the previous section.

Beyond the vision layer, the entire Qwen 2.5 generation offers additional features supporting content workflows:

• Text and image analysis - the model reads documents, scans, and graphic files, extracting structured and descriptive data from them.
• Image and video generation based on text descriptions.
• Web Search feature - searching the internet during a conversation, which allows augmenting responses with up-to-date information beyond the training data.
• Source code generation across multiple programming languages, along with code analysis and bug fixing.
• Multi-turn conversations while maintaining context.

Real-Time Voice Interaction with Qwen2.5-Omni-7B

Qwen2.5-Omni-7B expands multimodality into the audio channel: it takes text, images, video, and audio as input, and outputs text and audio simultaneously. Users can therefore hold a real-time voice conversation with the model - without speech-to-text transcription and voice synthesis operating as separate, sequential processes, as in traditional voice pipelines.

The model contains 7 billion parameters - significantly fewer than the flagship Qwen3.x or Qwen 2.5-VL variants. This compactness translates into lower voice response latency, making Omni-7B a practical choice for voice assistants and conversational interfaces.

Qwen vs. GPT-4 and Other Western Models: Benchmark Results

Qwen 2.5-VL outperforms GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash across four domains: video understanding, mathematical computations, document analysis, and question-answering accuracy. Alibaba documents these results with its own AI benchmarks, published alongside the launch of each subsequent Qwen-VL lineup, directly comparing them against models from OpenAI, Google, and Anthropic.

The table below compares the test domains against the reference models highlighted in Alibaba's official comparison materials.

These results apply specifically to the Qwen 2.5-VL generation and do not automatically translate to other variants in the family. Subsequent lines - Qwen3 and Qwen3.7 - are tested on different benchmark suites, often against newer reference models, such as Claude Opus 4. Independent verification of rankings is provided by comparison platforms like LMArena AI, where users evaluate responses from competing systems in a head-to-head mode - an assessment that is less dependent on the vendor's methodology.

A key methodological difference: the numbers provided by Alibaba are internal benchmarks, published as promotional material at the model's launch, rather than an independent third-party audit. Whether Qwen has truly "caught up" with or surpassed Western models depends on the test suite, the evaluation methodology, and the type of task. In mathematical reasoning and multimodal video analysis, Qwen-VL's advantage is repeatedly confirmed across multiple independent tests. In creative text generation and tasks requiring deep contextual knowledge of Western culture, GPT-4o and Claude still maintain an edge.

Application of Qwen Models in Digital Marketing, SEO, and Business

Qwen streamlines daily marketing operations and SEO optimization through advanced data analysis, agency process automation, and efficient code interpretation. The model works as a chatbot in the web application chat.qwen.ai and in the official Android app. Businesses primarily use it via the Alibaba Cloud API, integrating it with their own analytics systems and content marketing tools.

Specific use cases in marketing and SEO practice include:

• Content audit and optimization - the Web Search feature built into Qwen 2.5 browses the internet while responding to a prompt. Competitor analyses or content audits for keywords are therefore based on up-to-date data, not just training-period knowledge.
• Document and report analysis - Qwen 2.5-VL reads scans, graphic files, and corporate documents, extracting structured data from them. This comes in handy when analyzing Google Search Console reports, screenshots from SEO tools, or tender documentation.
• Automation of multi-step agency tasks - Qwen3.7 Plus executes long chains of actions without losing context. In marketing practice, this translates to autonomously running campaigns, monitoring rankings, and implementing on-page technical fixes as part of AI SEO.
• Code generation and debugging - the model writes and fixes code across multiple programming languages. This makes it easier to implement technical SEO changes (structured data markup, redirects, page speed optimization) without involving a developer for every minor tweak.
• Voice interfaces for customer service - the compact Qwen2.5-Omni-7B, thanks to low audio response latency, is well suited for building customer service voice assistants and sales hotlines where natural, fluid dialogue is essential.
• Large-scale business reasoning - Qwen3.7 Max, with a context window reaching roughly 1,000,000 tokens, processes entire marketing strategies, multi-volume market reports, or campaign repositories in a single pass. This allows for cohesive analysis without data fragmentation.

Western solutions like Copilot integrate primarily with Microsoft office suites and the Windows ecosystem. Qwen functions primarily as a hosted API service and a web chatbot - a more flexible starting point for integration with custom marketing tools and CMS systems than an off-the-shelf office productivity environment.

Open-Source Nature, Licensing, and Local Deployment of Qwen Models

Most Qwen models operate under permissive open-source licenses - you can run them on your own hardware or integrate them via an API, without using Alibaba's hosted chatbot. This applies to smaller variants like Qwen 3.5 9B as well as larger models in the Qwen3.x family, which are published alongside their weights and technical documentation.

Access to Qwen models follows three parallel paths. The official route is the chat.qwen.ai website and the Android app - both channels operate as hosted chatbots on Alibaba Cloud infrastructure. Developers and enterprises additionally turn to Alibaba Cloud as an API provider to integrate models into their own systems. Independently, the weights of many Qwen variants are shared on third-party platforms such as Hugging Face or Fireworks AI, where you can download the model to your own server or run it in a cloud other than Alibaba's infrastructure - without registering an account on chat.qwen.ai.

Open licensing sets Qwen apart from closed systems like GPT-4o or Claude, which are available exclusively through the provider's API. This has practical significance for companies that, due to regulatory or cost reasons, do not want to send data to external infrastructure: with local deployment, all inference traffic remains on hardware controlled by the organization.

How to Run Qwen Locally Using Ollama

Ollama runs selected Qwen variants as ready-to-download model images, without manual configuration of the Python environment or inference libraries. Simply install the application on your computer and pull the selected variant with a single terminal command.

The choice of variant dictates hardware requirements. Qwen 3.5 9B, a model with 9 billion parameters, requires noticeably less VRAM than the flagship Qwen3.x variants with tens of billions of parameters - it runs on a typical consumer-grade graphics card, and with fewer concurrent queries, even on a CPU. Once downloaded, Ollama exposes the model locally through its own API, compatible with a query format similar to the OpenAI standard, so connecting Qwen to existing applications does not require rewriting integration logic.

Local deployment via Ollama works well for tasks requiring repetitive, high-volume queries - mass product description generation or content classification - where the cost of hosted API queries would scale proportionally. The limitation is the lack of access to features dependent on Alibaba Cloud infrastructure, such as built-in web search: the local model operates solely on training-period knowledge and user-supplied data.

Limitations, Political Censorship, and Linguistic Characteristics of the Qwen Model

Qwen is subject to strict regulations and censorship by the government of the People's Republic of China - despite its global ambitions and support for over 100 languages. As an Alibaba chatbot, the model operates under the same content moderation mechanisms as other domestic AI systems, such as Ernie from Baidu or the Doubao family from ByteDance. This sets it apart from Western competitors subject to different regulatory regimes.

In practice, questions about even issues perceived by PRC authorities as sensitive - Tiananmen 1989, the status of Taiwan, or Tibet - trigger evasive, cut-off responses, or refer users to the "terms of service." This is not a feature unique to Qwen, but rather an effect of Chinese regulations regarding generative AI, applicable to all domestic providers of commercial language models - regardless of whether they operate as a hosted chatbot or release open source weights for local deployment. It is worth noting: describing this mechanism as "blocking topics inconsistent with the party line" is a journalistic oversimplification. Alibaba officially communicates it as compliance with local content moderation requirements, not as a declared ideological feature of the system.

Qwen's training corpus, dominated by Chinese and English data, gives the model a tangible advantage in tasks related to Chinese culture, idioms, law, and local context - an area where GPT-4o or Claude perform more weakly due to less exposure to Chinese-language data. For Polish and other European languages, Qwen handles basic communication and text generation well, but stylistic nuances, idioms, and deep cultural context appear average compared to models trained on corpora dominated by Western languages.

Infrastructure location represents another practical limitation. The hosted chatbot runs on Alibaba Cloud servers, which for companies in regulated EU sectors - finance, healthcare, public administration - may require an additional compliance analysis regarding GDPR and regulations on data transfers outside the EEA. The solution is the local deployment of open source models using tools like Ollama, described in the previous section: it eliminates the need to send data to Chinese cloud infrastructure, albeit at the cost of losing features dependent on the Alibaba Cloud API, such as built-in web search.

Alibaba's Impact on the Global AI Market and User Reviews

The expansion of Qwen models proves that Chinese AI ecosystems have not only caught up with Western competition, but in many aspects dictate the pace of technology development. Alibaba - globally known for the AliExpress marketplace - has invested resources in AI comparable to the budgets of OpenAI or Google, treating Qwen as part of a strategy to diversify revenue beyond e-commerce and logistics. This scale of investment explains the pace of releasing subsequent model lines: from Qwen3 through Qwen3.5 and Qwen3.6 to Qwen3.7 Plus/Max, at intervals counted in months, not years.

Qwen is not the only Chinese project changing the global perception of AI. DeepSeek built a reputation on low training costs for models with competitive quality, while Kimi AI develops conversational assistants with an emphasis on processing long context. Together with Qwen, they form a group of systems that in benchmarks - tests for Qwen3-235B-A22B or Qwen3-Max-Preview - regularly achieve results close to GPT-4o or Claude Opus 4. However, the comparisons themselves depend on testing methodology and do not represent an unambiguous industry consensus.

The practical entry point for users remains Qwen Chat, available for free via a browser at chat.qwen.ai - simply create an account, without installing a desktop application. This distribution model, based on free web access complemented by an Android app, lowers the barrier to entry compared to services requiring a paid subscription from day one. Users from outside China praise the response speed and the quality of generated text, while simultaneously raising concerns regarding content moderation on politically sensitive topics and the lack of a native mobile app outside the Android system.

The growing number of competing language models - Western and Chinese - increases the importance of strategies prepared for a multiplicity of engines responding to user queries. This is significant for companies focused on visibility in search engines and AI systems.

FAQ

Where can you test the Qwen model for free?

You can test the Qwen model for free at chat.qwen.ai (requires registration), in the Android app, as well as on the Hugging Face and Fireworks AI platforms.

Does Qwen handle Polish well?

Yes, the latest Qwen3 model line supports as many as 119 languages, including Polish, demonstrating excellent understanding of grammar and cultural context.

Are Qwen models safe for corporate data?

Yes. Thanks to the open weights of many versions, companies can run the Qwen model locally (e.g., via Ollama), which prevents sensitive data from being sent to external servers.