Qwen

Seeing the domestic AI companies spend money during this Lunar New Year, my first reaction wasn’t excitement, but familiarity. Tencent Yuanbao gave out a 1 billion cash red envelope on February 1st; Baidu Wenxin distributed red envelopes totaling 500 million from January 26th until mid-March; Alibaba’s Qwen launched a “treat plan” of 3 billion on February 6th; and Doubao leveraged the Spring Festival Gala for AI interactions to push its presence. My judgment is straightforward: this is still an inertial action left over from the previous era of the internet—first, pull people into the App, and second, build up usage frequency; everything else can wait. But the business of AI isn’t quite like a traffic-driven business.

Recently, I’ve been migrating some edge cases to MiniMax and local models. The more I use them, the more I feel that we shouldn’t always measure things by the standard of “the most powerful model.”

My judgment is straightforward: don’t force weak models into hard tasks. Models like MiniMax are indeed limited in capability, but for complex coding, long-chain reasoning, or ambiguous requirement decomposition, they fall a bit short. However, if you ask it to do data cleaning, document writing, or searching for proposal materials—these kinds of tasks—it can handle them perfectly well. The same logic applies to local models around the 12B size; translation, format rewriting, and batch cleaning are actually where they are best suited.

To put it plainly, it’s not that the models lack value; it’s just that we shouldn’t place them in the wrong roles.