What about data residency and retention? They quickly mention ZDR for enterprise accounts only on their support but that’s it. At least to us, that’s a hard showstopper.

Too bad they seem too busy to answer these questions, IMO that feels like a missed early leverage opportunity for them. We benchmarked a bit self hosted alternative, and nothing reach quite the cost/performance of Jev, yet.

If you want a local alternative; I added image support to OpenDecision three days ago - https://github.com/deepanwadhwa/OpenDecision

Videos will be next.

Cool to see another model with support for bounding boxes on images. Really wish to see segmentation in a future release; Gemini 3.5 Flash-Lite is mandatory in my use case as it's the best model for semantic image segmentation in my experience.

I love everything about this launch post. They feel like people i would get along with.

And i already sub to jev and i think this launch is going to make me a much more loyal user now lol

> We’ve heard you loud and clear on context lengths. Jev actually supports a much longer context length than we’ve enabled in production. We chose to limit it intentionally because Jev, like all models, is less reliable on longer contexts, and this constraint encourages more effective usage patterns in prod.

> We aren’t your parents though, so while we strongly recommend limiting context length, you no longer have to:

> TypeSafe-Danger-Zone-Long-Context: true

If you're looking for a decent local model, JevK5-4B is excellent. On my own benchmark for the things I work with (seeing if the answer to a question was correct given the evidence that was provided), it was within a point of jev.

> Warning: AI benchmarks. Please don't trust them

I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.

For anyone interested in a GUI around Jev I built Decision Studio for experimenting with it in our self-hosted OSS llms.py [1]

You can define tasks like sentiment analysis, support triage, or claim support, tweak the questions and criteria, and inspect the answers and probability distributions. You can also import existing recipes, save examples, and share recipes with recorded results.

An OpenRouter API key is required to use Jev.

[1] https://llmspy.org/docs/features/decision-studio

In case you need something a bit more lightweight, or want privacy. You can get amazing (better in a lot of cases) accuracy and performance from local models that run and train on CPU: https://jeffyclassify.com/blog/when-llms-fail-at-classificat...

Main thing I need from Jev is high rate limits. I have about 500-600 I need to fire off concurrently. I use Clef and now OpenAI decisions to help make up the difference.

Going from "I'll need a research team and 5 years" to Jev-based bird classifiers in just 12 years is really nice. I'm happy that some problems get easier, and I'm happy that people make things and share their work so we can all benefit.

[0]: https://xkcd.com/1425/

Slightly unrelated, but I really like the website styling. Looks like an improved version of Linux From Scratch dos.

I'm curious what people are building with these models

Shameless plug to my own inference engine: https://github.com/computerex/dlgo

- Inference for a bunch of open weights architectures

- Diffusion image generation fir Z ai turbo

- Vision input into LLM for inference

- Vulkan/fast CPU, optimized kernels

- Playground for testing

- Last but not least, system one fast inference mode with same API semantics as Jev

Decision models work by using a backbone model like qwen 3.5 9b, along with a special linear head that provides the readout for the forward pass.

Sounds like an incremental change with context size change, new "box" type, and composable images.

As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.

A lot of the week 1 Jev demos involved getting image data into it somehow, via description I guess? Should be interesting to see what we get with 100ms latency decisions on visual inputs. To me the latency / response time is just a huge game changer -- one to two orders of magnitude more responsiveness opens up a large design space.

Interesting way to release this, I'm not sure how to say this, but this has a real "enterprise"/old school taste to it verses how all the other models/AI companies are releasing things.

Very refreshing.

I have to admit, despite my initial scepticism of Jev, it is actually pretty fucking good at making decisions. I’m trying to break it with weird situations and I just can’t. And it’s so cheap!

I Love this > "Warning: AI benchmarks. Please don't trust them"

> Surprise: the Jev you all know and love (jev-1.13.0) was multimodal all along!

It really feels like they're just admitting that they had it disabled because they needed investors to subsidize turning it on.

Nice release post

How does JEV prevent distillation?

The font rendering on this site is something else. Is that a stroke being applied to every character? I thought this was a Linux glitch but it exists on iPhone too.

A shameless plug for my engine which supports different backends, and lets an LLM create rules on the fly for it

https://github.com/jlt-commons/lev

I'm aware this is an unproductive comment, but why are all their releases so cringe? It's like they're written by a Reddit mod or something...

> For the builders AND dreamers

> Enter the danger zone at your own risk.

> Size does matter, apparently

> We’re trying something new out here: we’re going to treat you guys like adults

> Lots of people love to ask what kind of sci-fi we’re releasing next

That said, their docs are great and don't seem to include all the Reddit cringe.

the discord jam was awesome! kudos to these guys!!

Ok. Obviously these are cool people trying to do something useful, and my real name is on this site so I definitely don't want to come off as hostile. But I have three problems with their whole... thing, here:

1. Adding bounding boxes only compounds the biggest issue with Jev, which is that it's using a massive model to do what we've been using purpose-built[1] models to do for years(/decades), only a little worse and a lot more expensive. This is fine --great, even-- if presented as a tradeoff.[2] And a product that enables "smart if statements" that are "sci-fi" does not sound like a tradeoff at all. In that light, ending the post with facetiously-labeled graphs that just show it beating a bunch of random names (including models with twenty-seven BILLION parameters) on unknown metrics feels borderline dishonest.

2. "We love the community's desire to help make models better!" feels like Manufacturing Consent but with way lower stakes. I turn on various experimental features and pick nightly builds and all that like many of us do, but this feels... well, at best it's kinda offputting rhetoric for a practice I otherwise condone. IMHO.

3. Their job postings go beyond groan-inducing to vaguely insulting. Take this "Data Demon" posting[3], which is presumably for a data scientist (data engineer...?) but is branded around mocking the Turing-award-winning scientists trying to warn the world about superintelligence. Yes, I had the same reaction: what? Why?? It's giving a corporate culture where A) executives feel entitled to shoot from the hip and inject their personal crusades on a whim, and where B) scientists/science isn't appreciated (or, worse: understood). Criticizing the concept of measurement isn't helping, nor is their choice to label output percentages as confidence scores.

I guess I hope I'm misunderstanding, cause they're gonna make millions regardless, I'm sure. I just know that I can't think of when I'd reach for Jev 1.*[4] for a prod usecase, and this is an awfully big company for a prototyping shortcut.

[1]: https://huggingface.co/models

[2]: of dev speed for cost and accuracy, which is lowkey another way to say efficiency and efficacy. Two pretty massive things to trade!

[3]: https://jobs.ashbyhq.com/typesafe-ai/d315d436-e517-4f17-82bb...

[4]: Which, btw, what happened to semver? Did we stop doing semver cause python did? Did I miss a memo/blog? Not a rhetorical question, I swear. Deprecating minor versions just seems unholy, much less minor version thirteen!

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So they held back a bunch of features because they knew they had basically no moat, and now they're trickling out a bit more functionality to try to convince people they are better than others and move fast? And using Apple advert slogans to show how cool they are?

I am starting to like Typesafe more and more lately. It's plainly simple but the next time I see the same problem, Jev just fits right.