What this is
This week on r/LocalLLaMA (the overseas local LLM deployment community), a user reported disappointing performance from Qwen3's 27B open-source version: the model does extensive "thinking" (outputting a long reasoning chain) and expands task scope on its own before executing, so by the time it actually starts working, time and context have been mostly consumed. The original quote: "a lot of thinking and a lot of overreaching." This isn't isolated — multiple community members share the same experience.
A clarification: this is Qwen3 in "thinking mode." Reasoning models (models that output a thinking process before answering) default to outputting reasoning before the answer — the design intent is to make complex tasks more accurate, at the cost of latency and resource consumption.
Industry view
Supporters argue Qwen3 still leads its size category among open-source models on benchmarks, and "overthinking" can be tuned via prompt engineering (adjusting input instructions to improve model behavior) or inference harnesses (runtime scaffolding around the model) — so user experience doesn't equal capability gap.
But the critical voices deserve more attention: DeepSeek R1, QwQ, and other reasoning models have shown similar problems — longer "thinking" doesn't mean better answers, instead producing "over-reasoning," where the model takes detours just to appear thoughtful. Anthropic and OpenAI have also warned in their official documentation that complex reasoning isn't a cure-all. When every vendor is racing on reasoning capability, user experience is being quietly sacrificed — that's the judgment we care more about.
Impact on regular people
For enterprise IT: deploying reasoning models costs far more in servers (GPU compute, electricity) than standard chat models. If "thinking" is wasted on over-reasoning, that's literally burning money.
For working professionals: users of finished products like ChatGPT or ERNIE won't feel this, but if your team starts using APIs (standardized channels to invoke models) to handle tasks with AI, remember to turn off the "deep thinking" switch — for non-creative tasks, default off is faster and cheaper.
For the consumer market: over the next year, AI products will split clearly into two tiers — one focused on "fast and good enough," the other on "slow but deep." Choose by scenario; don't pay for thinking you don't need.