Decision-1評測：微軟決策模型，價格完全複製Jev | Decision-1 Review: Microsoft Prices It Exactly Like Jev

10月 11, 2026

By Kit 小克 | AI Tool Observer | 2026-10-12

🇹🇼 Decision-1評測：微軟決策模型，價格完全複製Jev

微軟在 2026 年 10 月 9 日悄悄上線 Decision-1，這是一款專門用來「選答案」而不是「寫文章」的決策模型，定價精準複製了新創公司 TypeSafe 的 Jev：每百萬輸入 token 收費 0.042 美元、輸出完全免費——連小數點都一樣。

決策模型到底是什麼？

一般聊天型 LLM 一個字一個字生成文字，再靠你的程式解析回答；決策模型反過來做：你丟給它一個「狀態」（例如一封客服信、一段 JSON）加上幾個固定選項的問題，它一次幫每個選項打分、回傳機率，不產生任何自然語言。因為沒有輸出 token，速度能壓到幾十到幾百毫秒，比一般 LLM 的 1 到 3 秒快上許多。這類模型對應 AI 決策裡的「系統一」：快、直覺式判斷，像是分類客訴緊急程度、幫 Agent 從預設選項裡挑一個動作；真正需要深度推理、寫信回覆這種「系統二」的工作，還是得交給正規 LLM。

Decision-1 的底牌

Decision-1 其實是微軟拿阿里巴巴開源的 Qwen3.5-9B 做後訓練微調出來的專用模型，用途包括客服單路由、風險交易標記、Agent 動作選擇、品管驗證等高量重複性任務。微軟宣稱在 Xbox Research 的分類測試裡，速度比 GPT-6 Sol 快 35 倍，比自家 Quyet-1.0-Large 快 4.5 倍。

價格戰背後：開發者該怎麼看

有意思的地方不是技術突破，而是定價策略——微軟直接把 Decision-1 的價格抄到跟 TypeSafe 的 Jev 一模一樣，靠 Azure AI Foundry 的平台通路優勢去搶新創的市場，而不是打價格更低的仗。這對開發者是好消息：一個新興的小眾類別突然有兩個選擇，你可以直接比較延遲、信心分數校準和平台整合度，不用擔心單一供應商鎖死。

適合用：Agent 流程裡大量、低風險的判斷（路由、分類、信心門檻篩選）
不適合用：需要生成文字、深度推理或複雜上下文理解的任務
實務建議：把決策模型當成 Agent pipeline 前面的篩選層，把貴的 LLM 留給真正需要「想」的步驟

決策模型不會讓你的 Agent 變聰明，但能把本來浪費在「生成一堆文字只是為了選 A 還是 B」的成本砍掉一大半。好不好用，試了才知道。

🇺🇸 Decision-1 Review: Microsoft Prices It Exactly Like Jev

On October 9, 2026, Microsoft quietly shipped Decision-1, a decision model built to score fixed answer choices instead of generating prose — and priced it to match startup TypeSafe's Jev almost to the decimal: $0.042 per million input tokens, with output tokens free.

What Is a "Decision Model," Exactly?

A normal chat LLM generates text token by token, and your code has to parse the response. A decision model flips that: you hand it a "state" (a support ticket, a JSON blob, a screenshot) plus a set of typed, bounded questions, and it scores every allowed answer in parallel, returning calibrated probabilities — no generated text at all. Without output tokens to produce, latency drops to tens or low hundreds of milliseconds, versus the 1-3 seconds a typical LLM call takes. The category maps to "System 1" thinking: fast, intuitive calls like ticket routing, risk flagging, or letting an agent pick one action from a menu. Anything that needs real reasoning or drafting text — "System 2" work — still belongs to a full LLM.

What's Actually Under Decision-1's Hood

Decision-1 is a post-trained specialization of Alibaba's open-weight Qwen3.5-9B, aimed at high-volume, repetitive calls: routing support tickets, flagging risky transactions, picking an agent's next action, and quality-control checks. Microsoft claims Xbox Research saw 35x faster classification than GPT-6 Sol and 4.5x faster than its own Quyet-1.0-Large.

The Real Story Is the Pricing, Not the Tech

The interesting part isn't a technical breakthrough — it's the pricing strategy. Microsoft copied Jev's price point exactly and is using Azure AI Foundry's distribution reach to compete with a startup head-on, rather than undercutting it. That's good news for developers: a brand-new, niche category suddenly has two real options, so you can compare on latency, confidence calibration, and platform fit instead of worrying about a single vendor locking you in.

Good fit: high-volume, low-stakes calls inside an agent pipeline — routing, classification, confidence-threshold gating
Bad fit: anything needing generated text, deep reasoning, or rich context understanding
Practical takeaway: use a decision model as the cheap filtering layer in front of your agent pipeline, and save the expensive LLM calls for steps that actually require thinking

A decision model won't make your agent smarter — it just stops you from burning LLM tokens generating a paragraph when all you needed was "A or B." 好不好用，試了才知道 — you won't know until you try it.

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