Jev case: Another brilliant launch for developers: and its

Explained that Jev returns typed choices, scores, and confidence instead of tokens, letting support apps act on answers directly.

The 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

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@CompleteSkeptic34.6M views

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After 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 train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

From the same thread

• @DeRonin_ — 4 things to do when you get access to Jev: 1. don't replace your model, put Jev in front of it Jev makes the small choice first, and your expensive model only runs when it's really needed one email
• @aigclink — 前OpenAI研究员Diogo Almeida发布了一款新型模型：Jev，一种“放弃生成文字只输出类型化概率决策”的模型 称速度快40–200倍，输入成本便宜5–238倍，输出接近免费 就是说把大模型从会聊天会写长文的生成器，改成软件可以直接调用的概率决策函数 用并行概率决策替代逐token字符串生成，主打结构化、可校准、低延迟、低成本 创始人Diogo
• @teach_fireworks — 我快速看了Jev的一些公开帖子和官方文档说明以及例子，终于有人AI模型关心速度，幻觉和成本，而且Jev不是一个大语言模型，这是它能做到这么极致的原因！ 官方也给出了好几个例子，比如模型路由和LLM安全护栏。 所以在大语言模型狂奔3年后，AI模型的新的分叉出现了： 不是大尺寸，不是语言模型，极低的幻觉，成本，极快的速度和准确率，没有太多的推理。 几乎每一条都是和LLM反着来的。
• @0xPascual — Jev shipped from stealth last week and nobody's talking about it. Setup: One of the original ChatGPT co-authors spent two years building an AI that cannot generate text. Surface Story: It is not a

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