In the early hours of September 6th, Alibaba's (BABA) artificial intelligence subsidiary Qwen released Qwen3-Max-Preview (Instruct) on its official website—a preview version of a massive model with over one trillion parameters.

Alibaba (09988) claims this model demonstrates significant improvements in Chinese and English comprehension, complex instruction following, and tool calling capabilities (RAG/Tool-calling), while reducing knowledge hallucination phenomena in its design. The Preview version is now available for trial use and API calls on both Qwen Chat and Alibaba Cloud's model platform.

**Understanding the Model**

Alibaba positions Qwen3-Max-Preview as the "largest instruction-focused model" in the Qwen3 series to date, emphasizing two key aspects: prioritizing "instruction following and tool calling" as primary optimization targets, and opening deployment channels to both internal products (Qwen Chat) and commercial developers (Alibaba Cloud model services/Bailian platform).

These moves indicate that this massive model serves both as a product announcement and Alibaba's operational blueprint for advancing model-as-a-service offerings.

The model's highlights center on three verifiable facts: parameter scale (exceeding one trillion), accessibility through cloud platforms and chat products, and comparative advantages achieved on various public and private benchmarks.

**Strategic Thinking Behind Multiple Model Releases**

Alibaba CEO Eddie Wu previously stated publicly: "The company's primary goal now is to build an 'Artificial General Intelligence' (AGI) system that can ultimately surpass human intelligence capabilities. All Qwen3 models are open source, reflecting our long-term commitment to the open community and industry innovation."

Why did Alibaba prioritize "instruction + tools" this time? The Qwen team's previously proposed framework in Qwen3 technical reports (including thinking/non-thinking modes, hybrid dense and MoE architectures, and controllable thinking budget mechanisms) provided methodological foundations for the Max version's evolution.

Qwen3's technical approach doesn't simply pursue parameter count but treats "mode switching," "budget allocation," and "multimodal compatibility" as controllable variables. This enables more flexible adjustments for practical tasks when scaling to trillion parameters.

In Max-Preview's specific description, Alibaba lists reducing "knowledge hallucination" and enhancing "tool calling" as parallel core improvements: the former addresses output reliability and factuality (crucial for enterprise applications), while the latter directly relates to embedding large models into enterprise processes and reliability when calling retrieval/database/execution tools.

In other words, the productization path shifts from "better at speaking" to "better at doing" (actionable), representing Alibaba's technical logic for pushing models as platformized products to market.

**Competitive Landscape**

Recently, multiple domestic and international manufacturers have launched ultra-large-scale or AI Agent-focused models: Moonshot's Kimi K2, DeepSeek's V3.1, and international players like Anthropic's Claude Opus.

These models show significant differences in architectural choices (MoE vs Dense), actual activated vs peak parameters (Activated vs Total), and built-in support for Agent/tools.

Kimi and several domestic teams adopt MoE routes to reduce inference costs and improve single-model coverage; DeepSeek emphasizes hybrid inference modes (thinking/non-thinking) and rapid iteration within the domestic ecosystem; Anthropic positions AI Agent and long-term reasoning capabilities as differentiators.

In comparison, Alibaba's choice to deploy Max with "Instruct + tool calling optimization + commercially available platform" represents a strategy emphasizing usability and ecosystem integration.

Notably, absolute parameter values don't automatically translate to product advantages: MoE models can achieve extremely large scales in "total parameters," but actual activated parameters during inference are smaller, creating different cost structures—Alibaba hasn't disclosed activation parameter data for this massive model.

Additionally, open strategies (open source, preview, closed-source commercial) directly impact community ecosystems and secondary innovation speed. Alibaba's open-source practices and community accumulation with the Qwen3 series over the past two years determine Max's starting point for users and developers, fundamentally differing from completely closed-source competitor approaches.

**Betting on Practical Integration Value**

A trillion-parameter model launching in Preview form on Qwen Chat and Alibaba Cloud platforms means Alibaba treats this model as "platform capability": enterprises can embed the model into existing business systems like customer service, knowledge base retrieval, enterprise intranet search, and automated agents through APIs, RAG processes, and tool chains.

This path's commercial value lies not in single model sales but in long-term stickiness and value-added services through the platform, including retrieval, customized fine-tuning, tool chain hosting, and compliance governance.

Currently, Alibaba has e-commerce, finance, and enterprise service scenarios available for integration, with Max's capabilities as "better tool calling with fewer hallucinations" having clear landing scenarios.

For developers and third-party vendors, the Preview version serves as both a touchstone and threshold: the touchstone validates Max's performance in real data and business processes; the threshold comes from costs, integration complexity, and compliance requirements.

If Alibaba can provide low-cost engineering support in tool chain stability, retrieval reliability, and integration templates, it can transform technical advantages into ecosystem advantages.

**Industry Evolution**

From recent industry dynamics, the large model competition has shifted from individual models to overall system competition.

Alibaba's Qwen3-Max-Preview launch represents a clear acceleration in the race to "transform large models into enterprise-ready capabilities."

On September 5th, sources from a domestic clothing giant's CIO and HR director revealed that their company has rapidly reconstructed their entire business process—from trend identification to design, production, display, sales, feedback, and after-sales service—using DingTalk's complete GenAI toolkit on Alibaba's DingTalk platform.

This aligns with Alibaba's positioning of using GenAI technology in various forms to reshape B2B company operations, achieving Eddie Wu's stated "industry innovation" strategy.

This massive model release follows the same approach: shifting focus from pure parameter scale to "instruction following, tool calling, and hallucination reduction" for engineering usability, while rapidly gathering users and paid scenarios through Qwen Chat and Alibaba Cloud channels.

Parallel to this, industry representatives like Kimi, DeepSeek, and Anthropic are attempting to secure positions with their respective architectures, open strategies, and commercial approaches.

The ultimate winner won't be the one with the most parameters, but the one that achieves balance between model capabilities, compliance, engineering, ecosystem, and costs.

Further validation of Qwen3-Max's value requires time and third-party evaluation to verify its stability and cost-effectiveness in complex enterprise scenarios (long-term dialogue, tool chain calling, knowledge loops).

Meanwhile, regulation and platform governance will determine whether such massive models can exist long-term in larger-scale public and industry applications. Alibaba's move represents both a bet and a probe; the real variable lies in whether the ecosystem can be transformed into sustainable commercial and governance capabilities.