TypeSafe AI's Jev model is 40-400x cheaper and 20-200x faster than frontier LLMs
Jev: New frontier model 40-400x cheaper and 20-200x faster
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, has released Jev, a System One Model that makes fast, structured decisions without hallucination. Jev uses a parallel sampler and Reinforcement Learning for Calibrated Decisions (RLCD) to output type-safe values with confidence scores. It claims to be 40-400x cheaper and 20-200x faster than leading LLMs on workflow tasks, targeting automation where reliability and speed matter.
Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.
- jacobgold
First, congrats to the team on launching something genuinely interesting and new.
Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that.
This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do.
Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, but it's nothing like the code generating models we're all using today for code and automation.
Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. You can enforce structured output from an LLM too, with an appropriate harness, etc.
Assuming there's no funny business, the Doom demo is cool.
- mortsnort
I am confused why they say it is not an LLM and then in the documentation it is shown as being an LLM derivative. The documentation makes it sound like they're taking a pretrained LLM and then giving it their unique post-training. How is that not an LLM?
FAQ:
Is Jev just a smaller LLM?
Jev is neither small nor an LLM, hence being off the intelligence Pareto curve.
Image in documentation:
https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-prime...
- cfowles
Wasn't really till seeing this home assistant demo they have (https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4) that the value really clicked for me.
Seems really cool.
- futurisold
This, combined with contracts, could make a lot of things so much fun now!
For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/
. Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too (https://news.ycombinator.com/item?id=44399234).
I think I'm starting to glimpse the implications and it's gonna change agentic workloads if it holds up to scrutiny. It's too early for me to tell anything other than jot down some rough thoughts.
In short, you get blazingly fast semantic branching you can use in control flows. For contracts, I can now directly take the data model that you have to design and convert it into Jev's expected format. Or I can use Jev for semantic branching in postconditions.
If my understanding is correct, that should be doable, but I need to think more about it. It could be that with Jev I can finally “compile contracts” and better chain them into workflows, which is something I always wanted but didn't know how to do properly.
Eager to test. On the waiting list.
- maltalex
This is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or score) and quickly (milliseconds) and cheaply ($0.042/MTok) answers those questions.
Unfortunately, none of this is explained in the announcement, but the documentation [0] is pretty good.
[0]: https://docs.typesafe.ai/concepts/how-to-build-with-system-o...
- big_toast
It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing.
It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence).
Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model?
- lubujackson
After much fumbling around with prompts and evals, this is exactly how I am using LLMs in production, to narrowly make choices and return structured data. Any deterministic work gets pulled out of the prompt and my goal is to narrow the model output to be as clearly defined and as minimal as possible.
Jev's focus on structured I/O and confidence scores are game changing. If this does at all what it claims, I think this is going to quickly become the new standard approach for agentic systems.
- aryehof
As a zero-shot classifier, I expect that effectiveness is dependent on the data trained upon.
Jev input … > Unstructured data (e.g. text) with an emphasis on structured program state.
What model/training data is Jev based on? Surely result effectiveness is dependent on that?
- dgellow
Side note: it took me more time than I would like to admit to realize that Diogo Almeida isn’t a satirical version of the name Dario Amodei
- skerit
So in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?