This week, Alibaba's Tongyi Qianwen (Qwen) has dominated developer discussions—and we think it matters. A new release lets users set different "thinking depths" for the model. Simple questions get less thinking and faster responses; complex questions get more time to reason. The large model is shifting from "on or off" to "adjustable."

What this is

Over the past year, most mainstream models have treated "thinking" (i.e., the reasoning chain—the step-by-step logic the model runs before answering) as a switch: use QwQ or o1 for "thinking mode," use the standard version for "non-thinking mode." Qwen's approach is more like a dial—developers can manually specify how much compute the model spends on reasoning, calibrated to task difficulty.

Industry view

Supporters argue this matches reality: humans don't deliberate over every problem, and buying soy sauce doesn't require a thesis. What Alibaba offers is "on-demand allocation" flexibility—enterprises can tune costs by scenario. The skepticism is real: some developers question whether the actual differences between thinking tiers are as significant as advertised, and layering in another configuration adds burden on the API caller. The deeper risk: "thinking" itself remains a black box (users see the output, not the process), and the more tiers, the harder the debugging.

Impact on regular people

For enterprise IT: compute costs become predictable, and budgets can be tuned to business scenarios. For working professionals: when using an AI assistant, you may notice that sometimes answers come fast but shallow, sometimes slow but more accurate. For consumer markets: future C-end (consumer-facing) AI subscriptions may be tiered by "thinking depth"—basic tiers cheap, professional tiers expensive.