TypeSafe AI 发布决策模型 Jev,比 LLM 快 20-200 倍

Rohan Paul · @rohanpaul_ai · X·2026-09-16 03:39·8小时前
AI 导读

TypeSafe AI 发布新型 AI 模型 Jev,跳过逐 token 生成,速度比 LLM 快 20-200 倍、成本低 40-400 倍(输出 token 免费),响应时间 70-500ms。

Rohan Paul@rohanpaul_ai
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TypeSafe AI 发布决策模型 Jev,比 LLM 快 20-200 倍

2026-09-16 03:39· 8小时前
AI 导读

TypeSafe AI 发布新型 AI 模型 Jev,跳过逐 token 生成,速度比 LLM 快 20-200 倍、成本低 40-400 倍(输出 token 免费),响应时间 70-500ms。

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

Diogo AlmeidaAfter co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to t...