CASE FILE / Model & agent routing
Another brilliant launch for developers: and its 20-200x faster than LLM…
Another brilliant launch for developers: and its 20-200x faster than LLMs because it skips token-by-token generation entirely. TypeSafe AI just launched Jev, > 20-200x faster >40-400x cheaper (w/ output tokens free) > Frontier composable intelligence optimized for decisions So Jev is an AI model built to make software decisions instead of writing text for people. A normal LLM answers by generating tokens sequentially, so software often has to request structured output, parse it, validate it, and decide what happens next. Jev removes that translation layer: give it some data and a predefined question, and it returns a typed choice or score with probabilities and confidence. For example, a support app can ask whether a ticket is urgent, whether it violates policy, and which queue should receive it, then act on those answers directly. In code, Jev behaves like a smart if-statement: ordinary software controls the workflow while the model handles fuzzy judgments that rigid rules struggle with. Jev evaluates multiple structured questions in parallel rather than writing an answer token by token. TypeSafe reports 70-500ms responses and 40-200x faster performance than comparable LLMs
Original post
Another brilliant launch for developers: and its 20-200x faster than LLMs because it skips token-by-token generation entirely. TypeSafe AI just launched Jev, > 20-200x faster >40-400x cheaper (w/ output tokens free) > Frontier composable intelligence optimized for decisions So Jev is an AI model built to make software decisions instead of writing text for people. A normal LLM answers by generating tokens sequentially, so software often has to request structured output, parse it, validate it, and decide what happens next. Jev removes that translation layer: give it some data and a predefined question, and it returns a typed choice or score with probabilities and confidence. For example, a support app can ask whether a ticket is urgent, whether it violates policy, and which queue should receive it, then act on those answers directly. In code, Jev behaves like a smart if-statement: ordinary software controls the workflow while the model handles fuzzy judgments that rigid rules struggle with. Jev evaluates multiple structured questions in parallel rather than writing an answer token by token. TypeSafe reports 70-500ms responses and 40-200x faster performance than comparable LLMs
Chinese translation
对于开发人员来说，又一次精彩的发布：它比LLM快20- 200倍，因为它完全跳过了逐代币生成。 TypSafe AI刚刚推出Jev， > 20- 200倍快 >40- 400倍便宜（含免费输出代币） >针对决策进行优化的前沿可组合智能 因此，Jev是一个人工智能模型，旨在做出软件决策，而不是为人们编写文本。 正常的LLM通过顺序生成令牌来回答，因此软件通常必须请求结构化输出、解析它、验证它并决定接下来会发生什么。 Jev删除了翻译层：给它一些数据和预定义的问题，它就会返回带有概率和置信度的输入选择或分数。 例如，支持应用程序可以询问票证是否紧急、是否违反政策以及哪个队列应该接收票证，然后直接对这些答案采取行动。 在代码中，Jev的行为就像一个智能的如果陈述：普通软件控制工作流程，而模型则处理严格规则难以处理的模糊判断。 Jev并行评估多个结构化问题，而不是逐个标记地写答案。 TypSafe报告70- 500 ms响应，性能比同类LLM快40- 200倍
What this case shows
- Let JEV choose the model or tool first
- Auto-route on high confidence, escalate otherwise
- This is the most common production pattern