---
title: "System One Blog | Decision model articles"
description: "Explainers, comparisons, and FAQs about decision models and typed routing. Sample articles are marked and do not make product claims."
canonical: "https://system-one.dev/de/blog"
language: "de"
---

# System One Blog | Decision model articles

Explainers, comparisons, and FAQs about decision models and typed routing. Sample articles are marked and do not make product claims.

- [What Is a Decision Model? Typed AI Decisions vs LLM Text Generation](https://system-one.dev/en/blog/what-is-a-decision-model) — A decision model answers choice, score and yes/no questions with probabilities instead of generating text. Learn how it differs from an LLM and how to call one from TypeScript.
- [Jev Alternatives in 2026: Decision Models and a TypeScript SDK That Lets You Switch](https://system-one.dev/en/blog/jev-alternatives) — Comparing hosted and open decision models? Map the options by inputs, hosting and lock-in, and see how one TypeScript SDK lets you swap models by changing an ID.
- [An LLM-as-a-Judge Alternative: Use a Decision Model as the First Pass](https://system-one.dev/en/blog/llm-as-a-judge-alternative) — LLM judges are slow and can score the same answer differently. See which evaluations a decision model can take over, which it can't, and how to escalate low-confidence verdicts.
- [Too Many Tools? Route Agent Tool Calls with a Choice Question (TypeScript)](https://system-one.dev/en/blog/agent-tool-routing) — When an agent has dozens of tools, it picks wrong and burns context. Combine retrieval, lazy loading and a Choice question with a confidence threshold, with TypeScript code.
- [AI Agent Stop Conditions: Decide \"Done\" with Yes/No Probabilities](https://system-one.dev/en/blog/agent-stop-condition) — Agents loop forever or declare victory too early. Split \"done\" into yes/no checks, combine probability thresholds with deterministic tests, and keep a hard step limit.
- [LLM Model Routing: Judge Difficulty First, Then Send to a Small or Large Model](https://system-one.dev/en/blog/llm-model-routing) — Sending every request to your biggest model wastes money, and asking an LLM to route is slow. Use a decision model to score difficulty, route by threshold and escalate.
- [Microsoft-Decision-1 是什么？用途与常见问题](https://system-one.dev/zh-CN/blog/microsoft-decision-1) — Microsoft-Decision-1 是微软的决策模型，返回选项、评分和是非概率，而不是文本。本页整理微软官方说明中的用途与常见问题。
- [AI Agent Guardrails: Gate Risky Tool Calls with Yes/No Probabilities](https://system-one.dev/en/blog/agent-guardrails) — Guardrail frameworks tell you where to check, not how to decide. Turn a policy into yes/no questions and use probability bands to allow, escalate to a human or block.
- [Jev 和 ChatGPT 有什么区别？什么任务交给决策模型，什么交给大模型](https://system-one.dev/zh-CN/blog/jev-vs-chatgpt) — ChatGPT 生成文本，Jev 只回答类型化问题并返回概率。用一张表和一段 TypeScript 代码看清两者分工，以及怎么在 Agent 里组合使用。
- [Jev、Clef、Clef Flash、Clef Omni、Decider 怎么选？决策模型对比与积分对照](https://system-one.dev/zh-CN/blog/jev-vs-clef) — 五个决策模型用同一个 TypeScript SDK、同一种请求，切换只需换模型 ID。看输入类型、每块积分怎么对比，以及如何用自己的数据选模型。
- [决策模型和结构化输出有什么区别？用了 JSON Schema 还需要它吗](https://system-one.dev/zh-CN/blog/decision-model-vs-structured-output) — 结构化输出保证 JSON 格式合法，但不告诉你答案有多确定。看 enum、boolean、评分字段怎么换成决策模型问题，拿到每个选项的概率。
- [Jev 和 Claude 有什么区别？在 Claude Agent 里把判断交给决策模型](https://system-one.dev/zh-CN/blog/jev-vs-claude) — Claude 负责理解和写，Jev 只回答选一项、打个分或判断是否这类问题并返回概率。看 Claude 驱动的 Agent 里哪四个判断点适合交给 Jev，以及怎么用 TypeScript 接起来。
- [What Is Clairic? Extraction Models Explained](https://system-one.dev/en/blog/what-is-clairic) — Clairic is an extraction model that pulls typed values (text, numbers, dates, estimates) out of text, JSON and images, and returns null when the input doesn't determine a value. Value types, use cases, limits and pricing.
- [Clairic vs Jev and Clef: Extraction vs Decision Models, and How to Chain Them](https://system-one.dev/en/blog/clairic-vs-jev-clef) — Clairic pulls typed values out of text and images; Jev and Clef decide among options you list. One table of the differences, how billing differs, and the extract, decide, then reply pattern.
- [Prompting an LLM for JSON vs Using an Extraction Model](https://system-one.dev/en/blog/clairic-vs-llm-extraction) — Prompting an LLM for JSON vs a dedicated extraction model: null handling, confidence, spans, per-request billing.
- [OCR vs AI Field Extraction: Text Out or Fields Out?](https://system-one.dev/en/blog/clairic-vs-ocr) — OCR gives you text; Clairic gives you typed fields with confidence. Compare outputs, pipelines and when to combine them.
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