Track Hyper | Strategic Breakthrough Analysis of Tongyi Qianwen Trillion Model

Author: Zhou Yuan / Wall Street Insight

In the early morning of September 6, Alibaba's Tongyi Qianwen (Qwen) released Qwen3-Max-Preview (Instruct) on its official website—a preview version of a super-large model with over 1 trillion parameters.

Alibaba claims that this model shows significant improvements in understanding both Chinese and English, following complex instructions, and tool calling (RAG/Tool-calling), while also reducing the phenomenon of knowledge hallucination in its design; at the same time, the Preview version is available for trial and API calls on Qwen Chat and Alibaba Cloud's model platform.

What is this model?

Alibaba officially positions Qwen3-Max-Preview as the "largest and instruction-task-oriented" model in the Qwen3 series to date, emphasizing two points: first, that "instruction following and tool calling" are the main optimization goals; second, that the deployment channel is open to both its own products (Qwen Chat) and commercial developers (Alibaba Cloud model services/Bai Lian platform).

These two actions indicate that this super-large model serves both as a product claim and as an operational guide for Alibaba to promote model-as-a-service.

The highlights of this model can be verified on three levels: parameter scale (over 1 trillion), accessibility through cloud platforms and chat products, and comparative advantages achieved on several public or private benchmarks.

What is the underlying thought behind Tongyi Qianwen's recent launch of multiple large models with different focuses?

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

Why has Alibaba prioritized "instructions + tools" this time?

The framework proposed by the Tongyi team in the Qwen3 technical report (such as thinking/non-thinking modes, mixed dense and MoE architectures, and controllable thinking budget mechanisms) provides a methodological foundation for the evolution of the Max version.

The technical route of Qwen3 is not simply about chasing parameters, but rather treating "mode switching," "budget allocation," and "multimodal compatibility" as controllable variables, allowing for quicker and more flexible adjustments to actual tasks when scaled to trillions of parameters.

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

In other words, the productization path shifts from "being more talkative" to "being more actionable," which is the technical logic behind Alibaba pushing the model as a platform product to the market.

Wall Street Insight notes that recently, several domestic and foreign manufacturers have launched super-large-scale models or models aimed at AI Agents: for example, Moonshot's Kimi K2, DeepSeek's V3.1, and foreign models like Anthropic's Claude Opus Wait.

These models have significant differences in dimensions such as architecture selection (MoE vs Dense), actual activated parameters vs peak parameters (Activated vs Total), and built-in support for Agents/tools.

Kimi and several domestic teams have adopted the MoE approach to reduce inference costs and increase the coverage of a single model; DeepSeek emphasizes a hybrid inference model (thinking/non-thinking) and rapid iteration within the domestic ecosystem; Anthropic differentiates itself by focusing on AI Agents and long-term reasoning capabilities.

In contrast, Alibaba has chosen to launch Max in a way that emphasizes usability and ecosystem integration, using an "Instruct + tool invocation optimization + commercial platform" approach.

It is worth noting that the absolute value of parameters does not automatically equate to product advantages: MoE models can achieve a large scale in "total parameters," but the activated parameters during actual inference are smaller, resulting in different cost structures—Alibaba has not disclosed the activated parameter data for this super-large model.

Additionally, the open strategy (open source, preview, closed-source commercial) will directly affect the community ecosystem and the speed of secondary innovation. Alibaba has already accumulated open-source practices and community engagement with the Qwen3 series over the past two years, which determines Max's starting point for users and developers, fundamentally differing from completely closed-source competitors.

Is Alibaba betting on the integrated value of practical applications?

A trillion-level model launched in Preview form on Qwen Chat and Alibaba Cloud platform means that Alibaba is promoting this model as "platform capability": enterprises can embed the model into existing business systems such as customer service, knowledge base retrieval, enterprise intranet search, and automated agents through APIs, RAG processes, and toolchains.

The commercial value of this path lies not in single model sales, but in the long-term stickiness and value-added services brought by the platform, such as retrieval, customized fine-tuning, toolchain hosting, and compliance governance.

Currently, Alibaba has scenarios available for e-commerce, finance, enterprise services, etc., and Max, with its ability to "better invoke tools and fewer hallucinations," has clear landing scenarios.

For developers and third-party vendors, the Preview version serves as both a litmus test and a threshold: the litmus test can validate 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 toolchain stability, retrieval credibility, and integration templates, it can transform technical advantages into ecological advantages.

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

The Qwen3-Max-Preview launched by Alibaba is, in fact, a significant push by Alibaba in the track of "turning large models into usable capabilities for enterprises."

On September 5th, Wall Street Journal learned from the CIO and HR director of a domestic clothing giant that the company has quickly restructured its entire business process—from determining fashion trends to design, production, display, sales, feedback, and after-sales—using the full suite of GenAI tools provided by DingTalk on the Alibaba DingTalk platform This aligns with Alibaba's positioning to reshape B-end company practices with GenAI technology in different forms, achieving what Eddie Wu refers to as the "industrial innovation" strategy, which is consistent throughout.

The ultra-large model launched this time also follows the same thought or strategy: shifting the focus from purely parameter scale to the engineering usability of "instruction adherence, tool invocation, and reducing hallucinations"; at the same time, quickly gathering users and payment scenarios through Qwen Chat and Alibaba Cloud.

In parallel, different routes represented by Kimi, DeepSeek, and Anthropic are also trying to occupy positions with their respective architectures, open strategies, and commercial strategies.

The ultimate winner will not be the one with the largest parameters, but the one that can balance model capabilities among compliance, engineering, ecology, and cost.

To further assess the value of Qwen3-Max, time and third-party evaluations are needed to verify its stability and cost-effectiveness in complex enterprise scenarios (long-term dialogue, toolchain invocation, knowledge closure).

At the same time, regulation and platform governance will determine whether such ultra-large models can exist long-term in larger-scale public and industry applications. Alibaba's move is both an increase and a test; the real variable lies in whether the ecosystem can be transformed into sustainable business and governance capabilities