TypeSafe AI launches Jev, a text-free 'System One' model
1 Sep 15 2:00 PM · 8d ago · 7 articles · 8 posts · 48 comments · 4 sources · development 1 of 6
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released Jev in early access: a model that takes text input but outputs typed, calibrated probabilities instead of strings, trained with a new method called Reinforcement Learning for Calibrated Decisions (RLCD), claimed to be 20-200x faster and 40-400x cheaper than comparable LLMs at $0.042 per million input tokens.
“I am beyond excited to announce that today, TypeSafe AI is releasing our first System One Model”
Diogo AlmeidaDiogo Almeida Founder, TypeSafe AInandakishor_ml Creator of Laya (open-source rival)Simon Willison Independent AI commentatorflorianstandhar Creator of JevBench
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What was reported 9 claims about this development
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first by AIModels.fyi, 3d ago · also Arize AI, MarkTechPost
2 more headlines
- TypeSafe's Jev: Can decision models replace LLM judges? Arize AI · 3d ago
- TypeSafe AI Releases Jev: A System One Model That Returns Typed, Calibrated Decisions Instead of Text MarkTechPost · 3d ago
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first by Simon Willison, 2d ago · also HN Frontpage
1 more headline
- Jev introduces a new shape of LLM HN Frontpage · 2d ago
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first by The Indian Express, 2d ago · also Tom's Hardware
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first by Indian Express, 2d ago · also The Indian Express
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first by The Rundown AI, 8d ago · also RuntimeWire
1 more headline
- TypeSafe opens Jev early access for fast, typed AI decisions RuntimeWire · 8d ago
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2 outlets Introducing System One Models and Jev
first by HN Best, 8d ago · also TypeSafe AI
1 more headline
- Introducing System One Models & Jev TypeSafe AI · 8d ago
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first by YourStory.com, 2d ago
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1 outlet Reverse-engineered Jev-like model
first by HN Frontpage, 7d ago
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first by HN Frontpage, 8d ago
What people said 24 voices · best of 50 · verbatim
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"After two years in stealth, countless technical challenges, and research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our first System One Model: a new class of frontier models built to make fast, structured decisions that software can use directly." https:// typesafe.ai/blog/introducing-s…
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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…
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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...
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I'm trying to parse it down to what we had before vs what is new here.We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had1. "unstructured state in, typed probabilistic decisions out"…
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I think this is a great direction -- for some kinds of users. And this makes me wonder if the 'vs' framing is misleading.Yes, I think it's a mistake that many organizations are cramming LLMs inside of automated pipelines where the extreme generality/flexibility of the model is at odds with the fact that you're using it for a very specific task…
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I was thinking about something similar (maybe) - generally speaking, embeddings for LLMs tend to learn real world concepts - things like 'fruit' or 'France' or 'city' as directions in embeddings.But in things like programming, most concepts are abstract - 'if hungry eat an apple' in programming terms would look like'if hunger > 50 {apples--…
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The eval is baffling me> we assume there is a correct compute graph (a “workflow” represented in code) and use the predictions of the largest, smartest, and most expensive external models as reference probabilities. ... Rephrased: every model gets the same workflow. We test how they compare to the average of the smartest models (in this case…
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Could you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast?Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away.What class of memory might be useful here? Model gives probability…
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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…
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what is the…epistemic status, for lack of a better way to put it, of the probabilities? what do they mean? what (probabilistic) guarantees do we have about, say, the responses to- is the capital of france paris?- it is august. is it raining in paris?(forgive the examples; they're probably not semantically the sort of thing jev is trained to work…
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This is actually pretty cool. I think the undertalked about part of this for TypeSafe is that they can always "extract"/distill the frontier of this type of task from the newest LLMs for cheap. Jev seems seems to be GPT-6-Astra/Fable 5.1 but I imagine a bunch of training data is from earlier models?Then, you can serve it faster/cheaper than the…
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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…
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> Input tokens: $0.042 / MTok ($42 per billion tokens).> Output tokens: FREE (too cheap to meter).Insane. The video demos are really compelling, in particular the speed.> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. The surrounding code…
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I'm biased but I wouldn't call it misleading - generating text is super awesome and flexible, (we describe that in the blog post - and I personally use string models all the time) but it's true you pay a high tax for autoregressive generation> Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a…
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We've already started using it for some pretty powerful decision tree stuff. We're just scratching the surface. We shipped an extension for Swamp[1] a few minutes ago and the combination is great!The one downside is that the context window is very small (32k.) So some initial ideas we had for initial evaluation of code reviews won't fit yet in the…
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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]:
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I would love for things like this to be accessible via hubs like open router or AWS bedrock. It's hard to justify adding new model vendors directly with all the heightened concerns about privacy and security, but if bold new capabilities are added to a centralized already-vendor like AWS, technical people can adopt them without going through a…
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There's a whole lot of information on this page that doesn't tell me anything about what this actually is. Can anyone spell out what the architecture is here?They claim it's not an LLM, which I read as "not an auto-regressive token generator". I assume they are still using a transformer, otherwise they would be talking about the thing that's not a…
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If we could come up with a system to classify the probabilities across a large number of candidate words (or components thereof) then this could actually be good at producing text, one element at a time. We could call these elements 'tokens' and picking the right one could be called something like 'decoding'. Crazy idea but hear me out...On a more…
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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…
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This sounds good but so far all claims just sound like marketing terms. I'd love to see real proof. e.g. "RLCD" and "parallel sampling" have nothing to back it up.also "70-500ms vs 3-329 seconds" are apples-to-oranges unless the LLM baseline is doing comparable work (e.g., long chain-of-thought). If Jev is skipping generation entirely for a narrow…
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Hasn't there been a lot talk about Astra's opaque reasoning capabilities (being able to think through complex questions without using a chain of thought)?Given that, can't you just replicate Jev by telling Astra "here is the question, you must make a multiple choice decision / output a score between 1-10, please answer directly in a single word…
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We had early access and found it to be pretty useful. Having a second form of verification, where you can ask multiple questions (in the form of Nouls) raised our confidence in the outputs of other models. [0] IMHO This type of model works incredibly well in concert with LLMs, not as a replacement.[0]
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Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset…
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