Why Qwen3 Gives Me Great Hope for the Practical Application of AI?
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Why Qwen3 Gives Me Great Hope for the Practical Application of AI?
Aligning with developers is actually an unspoken core strategy of Qwen3.
Navigating Web3 tides with focused insightsAuthor:
Have you noticed that people seem to be getting numb to large models lately? At least I’ve seen a clear drop in traffic for related articles and social media buzz around model releases.
For example, recent launches of high-performing models with clear advancements—Qwen3, Gemini 2.5, GPT-4.1, and Grok-3—would have been explosive two years ago. But now, not so much.
Yet after talking to developers, I found the reality isn’t really “numbness,” but rather a shift from “spectator excitement” to “accelerated action.” The focus has moved from merely watching to actively building—the developer perspective is shifting: beyond raw model capabilities, the key question now is whether a model can significantly enhance their own work, or how well the model aligns with developers’ actual needs.
For instance, many founders and developers knew Qwen’s team was working on something big before Qwen3 launched. They weren’t just waiting—they started preparing over a month in advance, and switched their in-development AI applications to Qwen3 immediately upon release. In my recent conversations, Qwen3 comes up more and more frequently.
In their view, evaluating models solely by benchmark scores, as was common two years ago, no longer makes sense. With a clear development path now established—pre-training + post-training + reinforcement learning—most top models will eventually level out on standard benchmarks for tasks like coding or writing. More importantly, these benchmarks fail to reflect real-world usage, especially now that AI Agent applications are accelerating rapidly.
From this perspective, Qwen3 goes beyond basic capability improvements by deeply aligning with real developer needs and scenarios. It’s designed and refined specifically for ease of adoption by developers and enterprises.
Take one optimization goal of Qwen3: achieving strong performance at lower cost, making it easier for developers to adopt and use effectively. Behind this, Qwen3 involves extensive technical breakdowns and implementations. For example, while the 72B version of Qwen was most popular among enterprises, feedback indicated it required two H800 GPUs, which was inconvenient. In response, the Qwen team developed the more efficient 32B version, which developers now find easier to use.
This approach taken by Qwen3 is highly instructive—continuous optimization through alignment with real developer needs. As a result, Qwen3 is becoming the optimal solution for AI application deployment in enterprises and among developers. With this expectation in place, building AI applications on top of continuously and comprehensively evolving models has become the most certain strategy for developers and companies this year.
01
How to Align with Developers
Recently, OpenAI researcher Yao Shunyu (core author of Deep Research and Operator) discussed changes at the model level in his article “The Second Half of AI,” sparking what may be the broadest共鸣 among entrepreneurs and developers this year.
In his view, reinforcement learning has finally found a generalizable path—not limited to specific domains like AlphaGo defeating human players—but now achieving near-human performance across software engineering, creative writing, IMO-level math, and even mouse and keyboard operations.
Under these conditions, achieving higher scores on complex leaderboards becomes easier. In other words, this evaluation method is outdated. The real competition now lies in problem definition ability.
Seen this way, Qwen3’s true value becomes clear. While all top models perform strongly on benchmarks, a model ranking high on a benchmark isn't necessarily optimal for developers.
So what do developers truly care about in real-world scenarios?
Broadly speaking, it's model performance, cost, and ease of deployment. But in specific contexts, it comes down to the technical implementation of different models and their tools. This is why Qwen consistently pursues full-scale, full-modal exploration of intelligence limits and releases versions with various quantization levels, giving developers greater flexibility in choice.
A developer broke it down for me: Qwen3 includes eight models—two MoE (Mixture of Experts) models and six dense models—catering to diverse needs across different scenarios.
Among the dense models, the 0.6B and 1.7B versions are ideal for researchers, capable of running even without a GPU or dedicated graphics card, suitable for validating datasets and conducting data ratio experiments.
The 4B and 8B models are well-suited for consumer electronics and automotive industries, as they’re optimized for edge deployment—4B for smartphones, 8B for AIPC and smart cockpits.
The 32B model is widely favored for large-scale enterprise deployments. Meanwhile, the two MoE models can be directly scaled via servers, improving efficiency and enabling use in larger-scale applications.
He believes this approach is correct—only by addressing such granular combinations of needs can developers across various product domains have access to ready-to-use best-practice models, even if further customization is needed later.
This time, Qwen3 extends further in this direction. As China’s first hybrid reasoning model, it integrates fast, concise responses (fast thinking) with deeper chain-of-thought reasoning within a single model, unifying reasoning and non-reasoning models. Developers can even select their own “reasoning budget” to match diverse task requirements.
In enterprise settings, organizations typically fine-tune open-source models using their own data. For example, Qwen3 supports 119 languages. Though only released half a month ago in Japan, it’s already more popular than models like Claude and GPT-4o. Enterprises can inject Japanese scenario-specific data into the open-source Qwen3 base, achieving more flexible results than closed models with native Japanese support—delivering outsized impact with minimal effort.
Beyond these factors, developer sentiment toward Qwen largely stems from what they repeatedly emphasize: a solid base model (“good bottom model”).
A good base model ensures better outcomes when applying distillation, fine-tuning, post-training, or reinforcement learning. Especially under the scaling laws of reinforcement learning, a high-quality pre-trained model is one of the decisive factors for generalization. Notably, even in DeepSeek-R1’s paper, the distilled small model used Qwen as its base—leveraging reasoning data generated by DeepSeek-R1 to fine-tune the Qwen-7B base model, effectively transferring DeepSeek-R1’s reasoning abilities to Qwen-7B via knowledge distillation, resulting in excellent performance.
Members of the GeekPark team specifically discussed with Xu Dong, General Manager of Tongyi Large Model Business at Alibaba Cloud, what a “good base model” means from a developer’s perspective—and how it’s achieved.
Xu Dong believes every model improvement must manifest in two aspects: knowledge density and instruction following. This makes AI applications in previously difficult or low-success-rate areas more reliable—models are now more “obedient.” Through data engineering and algorithmic iteration, Qwen3 further enhances its performance in knowledge density and instruction following.
Now, Qwen3 can precisely extract 88 fields from 600-page tender documents by leveraging its high knowledge density and fine-tuned SFT training; in public opinion monitoring, it can abstract consumer reviews into standardized tags like “compact vehicles” or “sedans,” avoiding overfitting or oversimplification; in common intelligent customer service scenarios, Qwen3 accurately captures user intent, times product recommendations, and reduces customer churn.
As the industry rushes into the Agent era this year, Qwen3 timely upgraded its capabilities to meet Agent scenario demands—enhancing tool-calling and code generation abilities while strengthening support for MCP. Combined with the Qwen-Agent framework, which includes tool-calling templates and parsers, coding complexity is greatly reduced, making tasks like mobile and PC Agent operations increasingly feasible.
This optimization continues: last week, new features—Deep Research and WebDev (webpage generation)—launched on QwenChat’s official website. These are built on Qwen’s Agent framework. Qwen3 supports agent tool calling and natively supports the MCP protocol, achieving top-tier performance in BFCL evaluations for tool-calling capabilities.
Qwen3’s enhanced Agent capabilities are also delivering value across industries. For example, shortly after Qwen3’s release, Lenovo’s Baiying Intelligent Platform switched its underlying large model engine to Qwen3. As an IT solution, Baiying leverages Qwen3’s open-source nature, strong tool-calling and MCP support, and superior reasoning power to upgrade solutions for IT operations (AI services), AI office, and AI marketing. This enables SMEs in the AI era to DIY various Agents for vertical scenarios, transforming from providing production tools to directly delivering “digital employee” productivity—achieving significant cost reduction and efficiency gains.
Iterating models around developer scenarios and aligning closely with developers represent a necessary collective shift for large model vendors today.
Not long ago, Michelle Pokrass, core researcher on OpenAI’s GPT-4.1, pointed out that sometimes optimizing models for benchmarks yields seemingly good results, but real-world use reveals issues—like poor instruction following, odd formatting, or short context windows. Such feedback helps identify which evaluation metrics truly matter to customers. In her view, GPT-4.1 aims to make developers’ experience more pleasant. GPT-5’s current optimization focuses on enabling the model to autonomously distinguish between casual chat and deep thinking modes, reducing complexity and waste for developers.
Top models in both China and the U.S. are converging on this consensus—intentionally aligning with developers—making the upcoming wave of AI value realization highly promising.
02
Decoding Alibaba’s COT Before Blindly Jumping In
Through ongoing conversations with Qwen users, you’ll notice Qwen is starting to build a kind of fan-like trust. This trust fundamentally stems from consistent, “emotionally stable” growth.
Qwen updates monthly—within just half a month of Qwen3’s launch, the Qwen family had already rolled out several new models—outpacing even Llama in pace.
I recall Wang Tiezhen, Head of Hugging Face China, summarized why Qwen is popular in the Hugging Face open-source community: “plentiful, fast-updating, solid base model.” This consistency gives developers confidence that they’ll always have access to the latest, best, fastest models.
This phenomenon is interesting—AI application development is at least a decade-long, complex endeavor. Having confidence in a continuously invested-in model is crucial. We often say we should build AI applications as water rises and lifts boats; developers naturally want abundant, fast-rising, and endless water to build on securely.
No wonder Qwen has become the world’s most forked open-source model, establishing global influence. Perhaps recognizing that despite Meta’s commitment to open-sourcing Llama, its update speed and performance lag behind同期 closed models, Qwen sees an opportunity. If it can persistently deliver the best “weapons” quickly and openly—continuously releasing state-of-the-art models across all modalities and sizes—it should rightfully carry the open-source torch.
All such “ifs” require logical backing. So, does Alibaba’s internal chain-of-thought (COT) actually support this expectation of sustained, comprehensive open-sourcing of SOTA models?
In my previous analysis of Alibaba’s AI strategy, I outlined that due to Alibaba’s own business landscape, it will inevitably continue pushing the boundaries of intelligence. In the AI era, Alibaba’s mission to “make it easy to do business” must evolve into providing infrastructure for AI innovation and transformation across industries. This implies continuous evolution across all platform layers—from computing power to models to applications: Alibaba Cloud, the Qwen model family and its open-source ecosystem, and application platforms. The primary goal remains pursuing AGI to drive existing business transformation and enable AI-native applications.
Moreover, unlike Llama backed by Meta, Alibaba can afford to open-source costly SOTA models because it can close the loop via Alibaba Cloud. As the largest cloud provider in Asia-Pacific, this gives Alibaba the confidence to go all-in on open-sourcing. Many entrepreneurs and developers in the GeekPark community shared with me that while open-source models may appear revenue-free and brand-focused, Qwen’s open-source series has brought tangible revenue growth to Alibaba Cloud—arguably its best sales driver over the past year. Once you choose Qwen’s open-source models, buying Alibaba Cloud becomes a natural next step—since running Tongyi and its derivatives performs best on Alibaba Cloud.
The statement “Alibaba Cloud is the only cloud provider worldwide actively developing foundational large models and fully open-sourcing and contributing to them” reflects their ambition.
This is because MaaS (Model-as-a-Service) has become a key part of Alibaba Cloud’s business model. Looking at Alibaba Cloud’s growth over the past seven quarters, customers using Tongyi APIs tend to adopt many other cloud products—a clear cross-selling effect. For Alibaba, regardless of how models and AI applications evolve, AI and cloud infrastructure have a clear business model: the cloud ecosystem.
Qwen’s continued open-sourcing of SOTA models aligns not only with developers and customers but also with upstream and downstream ecosystem partners. That’s why on the very first day of Qwen3’s release, numerous device and chip companies—including NVIDIA, MediaTek, and AMD—announced support. In a way, open-source models’ biggest allies are NVIDIA and server vendors—better open models mean they can sell more GPU-powered appliances and hardware.
Clearly, only by fostering prosperity across the entire ecosystem can Qwen itself achieve value closure within Alibaba’s broader business loop. Under this logic, Qwen is naturally driven—“needing no whip to spur itself”—to lead the open-source SOTA charge. This is a reassuringly sound rationale.
Finally, developers now have a risk-free, no-brainer, stress-free way to “get free value”—an open-source model that serves as a stable, predictable technological foundation in the commercial world. This is critically important—and a major positive sign that AI application value realization is accelerating.
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