你的程式碼能讀取的決策
在呼叫前先定義可能的答案。Jev 會傳回具型別的選擇、分數或是/否機率,讓下游邏輯可以檢查已定義的結果,而不是從生成文字中擷取標籤。
聊天、程式開發代理與文件處理。以每 1M Token 計價 — 輸入、快取輸入與輸出分開計費。GPTProto 比官方費率低 20%。
| 場景 | TypesafeAI 牌價 | OpenRouter | GPTProto | 每月節省 |
|---|---|---|---|---|
| Personal10M tokens / 月 | $0.34 | $0.35 | $0.27 | −$0.07≈ $0.81 / 年 |
| Team100M tokens / 月 | $3.36 | $3.54 | $2.69 | −$0.67≈ $8.06 / 年 |
| Business500M tokens / 月 | $16.80 | $17.72 | $13.44 | −$3.36≈ $40.32 / 年 |
Jev Latest 是 TypeSafe AI 針對其目前穩定版 System One 模型所提供的動態別名。給它一個狀態與一組範圍明確的問題;取得你的應用程式可以檢查並據以行動的答案。當你需要分類記錄、選擇路由,或估計某個條件是否成立時,就可以使用它。請將工具、權限、算術運算與最終動作保留在你自己的程式碼中。
你的程式碼能讀取的決策
在呼叫前先定義可能的答案。Jev 會傳回具型別的選擇、分數或是/否機率,讓下游邏輯可以檢查已定義的結果,而不是從生成文字中擷取標籤。
三種問題類型
使用 Choice 從具名選項中選擇,使用 Score 取得已定義的量表分數,並使用 Noul 取得是/否機率。Choice 與 Score 也包含獨立的 confidence 欄位;Noul 則沒有。
多項判斷,單一狀態
在一次請求中,針對同一個狀態提出多個獨立問題。以支援工單為例,可以判斷部門、檢查急迫性,並為挫折程度評分,而不必為每個判斷分別傳送該筆記錄。
版本感知決策
jev-latest 可能會移轉到較新的穩定版本。請記錄每次結果所傳回的版本化模型 ID,並在決策閾值取決於一致行為時,鎖定已測試的版本。
TypeSafe AI calls Jev a System One model: its job is to evaluate bounded questions about supplied information, rather than write an open-ended response. An application can send a customer message, a retrieved document excerpt, or a structured object as state. Under questions, it specifies what to judge and, for Choice or Score, the permitted options or levels. Answers come back under the corresponding question IDs.
As of September 23, 2026, TypeSafe's jev-latest alias points to jev-1.13.0. The alias may advance without a change to your request. This matters for developers who set thresholds for automatic actions: retest representative cases when the underlying version changes, and keep the actual returned model ID in logs. The official request budget is 64K tokens overall, with a separate 32K limit for the state plus the longest individual question; remove irrelevant text even when a request fits those limits.
| Specification | Jev Latest |
|---|---|
| Provider | TypeSafe AI |
| Current stable alias target | jev-1.13.0 as of September 23, 2026; subject to change |
| Input | Text, or text-shaped state provided as a string, object, or array |
| Output | Typed decisions and probability values; not generated prose |
| Decision types | Choice, Score, Noul |
| Choice options | Up to 255 predefined options |
| Official request limits | 64K tokens total; state plus longest question up to 32K tokens |
| GPTProto model string and endpoint | See the live API Usage tab; confirm Jev's decision request format before publishing |
Support routing: Ask a Choice question for billing, technical, sales, or other, with a short definition for each. Send a separate Noul question about urgency. Route routine cases automatically only after measuring errors on your actual tickets; send uncertain or consequential cases to a review queue.
Agent action selection: Have an agent enumerate allowed actions, then ask Jev which option fits the observed state. Let the surrounding agent verify that the action is permitted, execute it, and check the outcome. Jev chooses from your options; it does not plan a whole task or run tools on its own.
Document triage: Classify an extracted text passage or score how closely it matches a rubric. If a document arrives as an image or PDF, extract its text first. Jev does not accept image, audio, or video input natively.
Both can participate in an agent workflow, but they expose different interfaces. Grok 4.7 is a generative model for coding, explanations, image understanding, and tool-using agents. Jev handles bounded judgments about text-shaped state. Treat the table as a task-selection guide, not a claim that either model wins the same benchmark.
| Decision factor | Jev Latest | Grok 4.7 |
|---|---|---|
| Best starting task | Pick an allowed action; classify or score supplied state | Write code or explanations; reason through broader work |
| Input | Text or structured text-shaped state | Text and images |
| Result | Typed Choice, Score, or Noul; probabilities | Generated text; structured output and tool calling are supported |
| Agent role | Decision component inside a larger workflow | Generative worker or reasoning component |
| Appropriate test | Accuracy, calibration, abstention policy, latency, cost per decision | Task completion, tool behavior, output quality, latency, total cost |
For a mixed workflow, a generative model can draft a reply after Jev has routed a ticket. The application still owns policy checks, review thresholds, and any final action. Evaluate both models on the same real records before moving traffic; their API prices and output types are not directly interchangeable.
Write one narrow question for each judgment. For a refund workflow, ask whether the customer requests a refund and which department should receive the ticket. Let code check order totals, eligibility dates, and authorization rules. TypeSafe documents weaknesses in arithmetic, counting, date comparisons, indirect questions, and long states filled with irrelevant material.
Define an other option for Choice when the allowed categories may not cover every case. Test common ambiguous inputs and adversarial text. Review the model's probabilities on labeled examples before selecting a threshold: a high confidence score is a signal for routing, not proof that the decision is correct. Log the question wording, returned version, decision, and later outcome so you can find regressions when an alias changes.
與本模型相關的指南、對比與更新。
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