Aligning with developers is actually a core strategy of Qwen3 that has not been fully recognized.
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Have you noticed that people seem to be getting a bit tired of large models lately? Personally, I've observed that the traffic for articles on related topics and the buzz on social media regarding models has noticeably decreased.
For instance, the recent releases of excellent models like Qwen3, Gemini2.5, GPT-4.1, and Grok-3, all with significant new developments, would have caused a huge stir if it had happened two years ago.
However, after asking around in developer communities, I found that the actual situation is not one of "fatigue," but rather a shift from "observational excitement" to "accelerated action." Developers are moving from merely "watching" to "doing," changing their focus to whether the model can significantly enhance their work or how well the model aligns with their needs.
For example, many entrepreneurs and developers around me were aware that the Qwen team was preparing something big before the release of Qwen3. They had been "waiting in the wings" for over a month and immediately switched the models behind their AI applications to Qwen3. Recently, when discussing the new changes at the model level, I found that Qwen3 was mentioned more and more frequently.
In their view, simply evaluating model performance based on scores, as was done in the past two years, has lost its significance. With a clear path for model capability improvement—pre-training + post-training + reinforcement learning—many evaluation benchmarks for specific abilities like coding and writing are leveling out. More importantly, these benchmarks no longer reflect the actual scenarios in which models are used, especially after this year accelerated the application of AI Agents.
From this perspective, Qwen3 has made significant "alignments" with the real needs and scenarios of developers beyond just improving the model's foundational capabilities. It can be said that it is designed and refined specifically for easy adoption by developers and enterprises.
For instance, one of Qwen3's overall optimization goals is to achieve strong performance at a lower cost, making it easier for developers to use effectively. Behind this, Qwen3 has undertaken extensive goal breakdowns and technical implementations. For example, the most popular model size among enterprises was 72B, but after receiving feedback from developers that 72B required two H800s to run and was inconvenient, the Qwen team explored a more efficient 32B model, which developers found much easier to use.
This path taken by Qwen3 is quite enlightening. By continuously optimizing through alignment with developers in real scenarios, Qwen3 is becoming the "optimal solution for AI application implementation" for enterprises and developers. With such expectations, following the continuous and comprehensive iteration of the model, developing AI applications has become one of the most certain tasks for developers and enterprises this year.
01
How to Align with Developers
Recently, OpenAI researcher Yao Shunyu (a core author of Deep Research and Operator) discussed the changes at the model level in his article "The Second Half of AI," which has resonated widely among entrepreneurs and developers this year.
In his view, with reinforcement learning finally finding a path to generalization, it is no longer effective only in specific domains, such as defeating human chess players with AlphaGo, but can now approach human competition levels in software engineering, creative writing, IMO-level mathematics, mouse and keyboard operations, and more.
In this case, competing for leaderboard scores and achieving higher scores on more complex lists will become easier. In other words, this evaluation method is outdated; what is now being competed is the ability to define problems.
From this perspective, the true value of Qwen3 lies in this understanding of the model. Because while models perform strongly in benchmark evaluations, a model that ranks high in a benchmark may not necessarily be the best for developers.
In this context, what do developers value more in practical scenarios?
On a broad level, it is likely model performance, cost, ease of deployment, and other aspects. But in specific scenarios, it comes down to the technical implementation methods of different models and their tools. This is why Qwen has consistently adopted a full-size, full-modal exploration of intelligent limits and has released different quantization precision model versions to give developers greater freedom of choice.
One developer broke it down for me, saying that the Qwen3 series includes eight models, comprising two MoE (Mixture of Experts) models and six dense models, catering to different needs in various scenarios.
Among the dense models, the 0.6B and 1.7B models are particularly suitable for researchers, as they can even run without a GPU or dedicated graphics card to validate some datasets and perform data ratio work.
The 4B and 8B models are suitable for the consumer electronics and automotive industries, as these two models are appropriate for end devices; the 4B model is suitable for mobile phones, while the 8B model can be integrated into AIPC and smart cockpits.
The 32B model is widely popular for large-scale enterprise deployment. Additionally, the two MoE models can be directly deployed at scale via servers, improving utilization efficiency while being applicable in larger scenarios.
He believes this approach is correct because only by considering the most segmented combinations of needs can developers working on different products in various scenarios have access to a best-practice model that is ready to use, even if they still need to DIY later.
This time, Qwen3 has further extended in this direction, being the first mixed inference model in the country, integrating fast thinking with quick and simple responses and deeper reasoning capabilities into a single model, achieving unification of inference and non-inference models. Developers can even choose their "thinking budget" to adapt to diverse task requirements.
In enterprise scenarios, models are generally fine-tuned based on open-source models combined with their own data. For instance, Qwen3's model upgrade supports 119 languages. Although Qwen3 was only released half a month ago in the Japanese market, it has already become more popular than models like Claude and GPT-4o. This is because enterprises are injecting Japanese scenario data into the open-source Qwen3, making it more flexible than a closed-source model that only supports Japanese, achieving a significant effect.
Of course, beyond all this, developers' attitudes toward Qwen largely stem from what they mention most—having a good base model.
A good base model means that distillation, fine-tuning, and reinforcement learning on the foundational model will yield better results. Especially for reinforcement learning, the Scaling Law requires a high-quality pre-trained model, which is one of the decisive factors for model generalization. I recall that even the distilled small model shown in the DeepSeek-R1 paper chose Qwen as the base model, using the inference data generated by DeepSeek-R1 to fine-tune the Qwen-7B base model, transferring DeepSeek-R1's reasoning capabilities to Qwen-7B through knowledge distillation, resulting in excellent model performance.
The Geek Park team members and Xu Dong, General Manager of Alibaba Cloud Tongyi's large model business, specifically discussed what a good base model means in terms of developer experience and how it is achieved.
Xu Dong believes that every improvement in model capability will be reflected in two aspects: knowledge density and instruction adherence. This makes it so that in the past, some AI application scenarios that were difficult to achieve, had low success rates, or required "gacha" mechanics, are now more "compliant." Qwen3 has further improved its performance in knowledge density and instruction adherence through data engineering and algorithm iteration.
Now, Qwen3 can rely on its strong knowledge density and refined training during the SFT phase to accurately extract 88 fields from a 600-page bidding document in data mining tasks; in public opinion monitoring scenarios, Qwen3 can abstract consumer evaluations into standardized labels like "small vehicle" and "sedan," avoiding overfitting or vague generalizations; in more common intelligent customer service scenarios, Qwen3 can accurately capture user needs and guide product recommendation timing, reducing customer churn rates.
As the entire industry begins to sprint into the Agent field this year, Qwen3 has also timely raised the capability requirements for models in Agent scenarios, optimizing Agent tool invocation and coding capabilities while enhancing support for MCP. Combined with the Qwen-Agent framework, which encapsulates tool invocation templates and parsers, the complexity of coding has been significantly reduced, making tasks like mobile and computer Agent operations more feasible.
This optimization is ongoing. Last week, on the official QwenChat webpage, we saw the launch of two features: Deep Research and WebDev, both implemented based on the Qwen Agent framework. Qwen3 supports agent tool invocation and natively supports the MCP protocol, performing the best among top models in the BFCL evaluation of tool invocation capabilities.
Qwen3's enhanced Agent capabilities are also playing a role in customer scenarios across various industries. For example, after the release of Qwen3, Lenovo's Baiying intelligent agent platform quickly switched to their underlying large model engine. As an IT solution, the Baiying platform utilizes the open-source nature of Qwen3 and its support for agent tool invocation and MCP, along with stronger reasoning capabilities, to upgrade solutions for IT operations (AI services), AI office, AI marketing, and other scenarios, enabling small and medium-sized enterprises to DIY various Agents in vertical scenarios in the AI era, achieving a leap from providing production tools to directly delivering "digital employee" productivity, further realizing cost reduction and efficiency enhancement.
Focusing on further iterations of models around developer scenarios and aligning with developers is actually a collective shift that large model vendors need to undertake recently.
Not long ago, OpenAI GPT-4.1 core researcher Michelle Pokrass also pointed out that sometimes adjusting models to optimize benchmark tests may look good on the surface, but actual usage reveals issues, such as models not following instructions, having odd formats, or having too short contexts. This feedback has clarified which evaluation metrics are truly important for customers to optimize. In her view, the goal of GPT-4.1 is to make developers feel pleased when using it. The current optimization goal for GPT-5 is to enable the model to distinguish when to engage in conversation and when to engage in deep thinking, reducing the complexity and waste issues brought to developers by OpenAI's model supply.
Outstanding models from both China and the U.S. are beginning to share this consensus, consciously aligning with developers, so the upcoming realization of AI value will definitely be a positive development.
02
Decoding Alibaba's COT (Chain of Thought) Before "Mindless Entry"
In the process of gradually communicating with developers using Qwen, you will find that Qwen has begun to develop a trust akin to a fan effect. This trust fundamentally stems from long-term "emotionally stable" growth.
You will find that Qwen updates every month, and even just half a month after the release of Qwen3, the Qwen family has already updated several models, which is "more diligent" than Llama.
I remember Wang Tiezhen, the head of Hugging Face in China, summarized the reasons why Qwen is popular in the Hugging Face open-source community as "large quantity, fast updates, and good base model." This certainty reassures developers that they will continuously have the latest, best, and fastest models at their disposal.
This phenomenon is quite interesting. AI applications represent a relatively long-term and complex construction for at least the next decade, and having a model that is continuously invested in is crucial. We often say that as the tide rises, so too should AI applications; developers of AI applications will certainly hope for a large flow of water, a fast rise, and a continuous source to feel more secure in their work.
This is likely why Qwen has become the open-source model with the most derivative models globally, establishing its own global influence. It seems to have recognized that while Llama insists on being open-source, its update speed and performance lag behind those of closed-source models from the same period. If Qwen can consistently and rapidly provide everyone with the best "weapons," continuously open-sourcing state-of-the-art (SOTA) models in all modalities and sizes, then Qwen should be the one to carry the open-source banner.
All "ifs" must be supported by a logical chain. Therefore, whether Alibaba will firmly support Qwen in continuously and comprehensively open-sourcing SOTA models must be examined in light of whether Alibaba's own COT aligns with this expectation.
In a previous article analyzing Alibaba's AI strategy, I outlined that due to Alibaba's own scenarios, it will certainly continue to explore the limits of intelligence. In the AI era, Alibaba's extension of "making it easy to do business anywhere" will inevitably provide infrastructure for AI innovation and transformation across various industries. This means that every layer of platform opportunities—from computing power to models to applications—needs to continuously evolve, including Alibaba Cloud, the Qwen model family, and its open-source ecosystem, as well as application platforms. The primary goal must be to pursue the realization of AGI, thereby breaking through the existing business's AI transformation and upgrading and AI-native applications.
Moreover, unlike Llama, which is backed by Meta, Alibaba can still achieve a business closed loop even by open-sourcing the costly SOTA models. As the largest cloud provider in the Asia-Pacific region, this gives Alibaba the confidence to be resolutely open-source. Many entrepreneurs and developers in the Geek Park community have shared with me that although open-source models may seem unprofitable and focused solely on technical branding, the Qwen series of open-source models has brought tangible revenue growth to Alibaba Cloud, making it arguably the best sales performer for Alibaba Cloud in the past year. Choosing the Qwen open-source model naturally leads to purchasing Alibaba Cloud, as running the entire Tongyi and its derivative models on Alibaba Cloud is the most efficient.
The statement "Alibaba Cloud is the only cloud computing provider in the world actively developing foundational large models and contributing comprehensively to open-source" reflects their goals.
This is because MaaS has already become a very important component of Alibaba Cloud's business model. From the growth of Alibaba Cloud over the past seven quarters, customers using the Tongyi API have significantly driven the usage of many other cloud products, demonstrating a clear effect of customer cross-selling. For Alibaba, regardless of how model capabilities and AI applications evolve in the future, AI and cloud computing infrastructure have a very clear business model—cloud computing networks.
Qwen's continuous open-sourcing of SOTA not only aligns with the interests of developers and customers but also with the interests of the upstream and downstream ecosystems. This is why, on the very first day of Qwen3's release, many terminal and chip companies announced their support for the Qwen3 model, including companies like NVIDIA, MediaTek, and AMD. To some extent, the biggest friends of open-source are NVIDIA and server manufacturers; with the best open-source models, they can sell all-in-one machines and more GPUs.
It is evident that only by promoting the prosperity of all upstream and downstream ecosystems can Qwen's own value be realized within Alibaba's larger business closed loop. Under this logic, Qwen must "spur itself on" to carry the open-source SOTA banner, which is a reassuring logical chain.
Finally, there is a way for developers to "mindlessly enter," with no risks and no pressure to "take advantage," allowing open-source models to become a stable technical foundation in the commercial world. This is very important and represents a significant boost to the acceleration of AI application value realization.
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