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Comparing: Ask AI One Question, Get an Essay — Alibaba's Qwen 3.8 Has a Fix & 通义千问 27B 新模型很强,但默认"过度思考"是个被忽视的隐患

AEN
Qwen国产大模型自由职业·

Ask AI One Question, Get an Essay — Alibaba's Qwen 3.8 Has a Fix

Today I asked AI to help me reply to a client's quote. It wrote 1,500 words.

Last Wednesday, I was in a coffee shop helping a designer friend respond to a regular client's pricing inquiry. I just wanted AI to polish the line "Total 8,000 yuan, paid in two installments." Instead, it spat out 1,500 words — from design philosophy to industry trends, ending with a section on "recommended communication strategy." I just stared at the screen. Same thing happened to me with GPT-4 — ask a simple question, it warms up with "first... second... finally..." I got stuck on this too: I figured shorter questions get shorter answers. The opposite is true.

Alibaba's just-launched Qwen 3.8 27B: useful, but "over-thinks" by default

Qwen is Alibaba DAMO Academy's open-source model series. In the recently released 3.8 version, the 27B (27 billion parameters) tier has been called by several reviewers as close to GPT-4 level. But it has a quirk: in default mode, it over-thinks everything — even "what's 1+1" gets three paragraphs of derivation.

My peer Xiaolin (freelance translator, uses AI to proofread) tried it last week. She said when she asked "is this sentence smooth?", Qwen 27B also explained the grammar before responding. Xiaolu (Xiaohongshu personal brand, 30k followers) told me she uses it to write titles faster than her previous tools — "ask anything, get the answer, no lecturing" — as long as she remembers to add "answer briefly."

How low is the barrier to try it today

Money: 0, model is free
Time: sign-up to first chat, 5 minutes
Technical bar: just need to use a browser, no code required
First step: go to qwen.ai, click "Chat" at the top, first ask it "what's 1+1" and see if it rambles

If it rambles, add "answer in one sentence" or "no analysis" to the end of your question. I had to mess this up three times before I learned.

Advice by stage

Just starting (solo, no clients yet): Your current tools are enough. AI isn't your bottleneck — get the client and product flow working first.

1-2 steady clients: I'd suggest spending 10 minutes trying it. Focus on short-and-quick tasks — replying to messages, proofreading, title brainstorming. Leave complex work to GPT or Claude.

Scaling up (more projects coming in): Every minute saved is money. You can have an assistant or outsourced partner use it for repetitive work, but I wouldn't risk important client deliverables on a free model myself.

Whether to try depends on how busy you are. AI tools drop dozens a month — missing one isn't a loss.

BZH
通义千问阿里Simon Willison·

通义千问 27B 新模型很强,但默认"过度思考"是个被忽视的隐患

这是什么

阿里这周发布 Qwen 3.8 27B(270 亿参数版本),知名开发者 Simon Willison(Django 框架共著者,长期评测 AI 模型的技术博主)给出的评语是:"模型本身很优秀,但默认过度思考。" 所谓"过度思考",指模型出厂默认开启 Chain-of-Thought 推理链(即在回答前先在内部一步步推演),即使面对简单问题也要消耗大量 token(模型处理和计费的最小文本单位)和等待时间。结果是:用户问得越简单,钱花得越冤枉,响应也越慢。

行业怎么看

这件事值得我们专门拎出来看,因为它不是孤例,而是开源中小模型近半年来的一个普遍症状。 乐观一方会说:27B 能跑到这个水平本身就是胜利——小参数路线在逼近闭源大模型;开发者只需在调用层关掉推理链即可。Simon Willison 自己的倾向也偏这一边。 但反对声音更值得听:模型"默认过度思考",说明厂商在按 benchmark(跑分测试集)调优,而非按真实使用成本调优。一个被训练成"先穷举再收敛"的模型,部署到生产环境(真实业务场景)后,每一次 API 调用都在烧钱。如果再用 Agent 架构(让 AI 自主串联多个工具完成多步任务),每一步决策都会被这种倾向放大。 我们认为更大的隐患在生态层面:如果开源社区把这种默认配置当成事实标准,"过度推理"会成为整个生态长期承担的隐性税——因为没人主动去关掉一个藏在默认值里的开关。

对普通人的影响

- 对企业 IT:我们建议选开源模型搭内部问答或知识库前,先压测 token 消耗和响应延迟——"跑分强"和"用得起"是两件事。 - 对个人职场:如果你在用 Qwen 系列 API 做工作流,记得在调用层关掉默认推理链(通常是一个开关参数),这是当下最直接的省钱手段。 - 对消费市场:未来 AI 产品会分化成"快答型"(便宜、快、不一定准)和"深思型"(贵、慢、更稳)两类,用户会被训练出"什么时候该等"的判断力。