← Back to Blog Laya: one more System One model
Just a couple of days after Jev shipped, it turned out it wasn’t alone in its category. Another System One model showed up, Laya from ConvAI Innovations, and this one is open source. You can download it from Hugging Face for free and run it locally, on a server or just on your laptop.
A quick reminder of what this class of models is. They don’t generate text. Instead, they produce a typed decision right away, along with a confidence percentage for it. They’re useful anywhere you need one specific answer instead of a wall of random text: rating how critical a bug is the moment the ticket gets created, filtering out spam and toxic comments, or deciding in a support chatbot whether it can answer from the knowledge base itself or it’s time to pull in a human.
Everything below is a breakdown based on public material: docs, articles, other people’s examples, and numbers from comparison writeups.
What they have in common
Jev and Laya both work on the same principle: you give the model context and a question, and you get back one of three things:
- a yes/no value with a confidence percentage attached to it;
- a pick from a predefined list of options, with a confidence percentage for each one;
- a number on a scale, say bug severity from 0 to 5.
No explanation, no reasoning, just a fast, specific answer.
Where they differ
Jev is only available through a third-party provider’s API, every request goes to their server and back, which is 230 to 280 ms on a decent connection, but can be a lot more when the network is bad. Laya runs locally, on a server or on a laptop, weighs about 1 GB, and can return an answer in 30 to 40 ms. That’s a 6 to 7 times difference.
You pay for that speed and privacy with accuracy and context size. Laya’s context window is only 512 tokens against roughly 4000 for Jev, a long ticket or email just won’t fit in whole.
On accuracy, Laya holds up fine on simple binary splits, but the more options there are to pick from, the worse it gets: with 50 to 100 of them and no fine-tuning, it guesses right about 42% of the time, while Jev is around 87% on the same data.
One more difference, and one of the main ones, is confidentiality. Every request to Jev goes to another company’s servers. With Laya the request stays inside your own network, nothing leaves it. For closed corporate applications where data can’t go outside at all, the choice between Jev and Laya comes down to this one point, and the rest stops mattering.
The debates around Laya
The dispute over authorship explains how Laya appeared so fast. The developer Nandakishor M. says he published a paper on RLCD, the mathematical approach these models are built on, back in early 2025. When TypeSafe AI released Jev as a closed paid product without crediting that work, he put Laya together in a couple of days and posted it on Hugging Face as a free alternative. The project quickly picked up thousands of stars on GitHub, and on Reddit, mostly in r/LocalLLaMA, a discussion flared up over who copied whom.
As for which model to pick, the community has already settled into two camps, and both make sense in their own way. Some go with Laya for independence and privacy, the typical argument being that it’s better to run a model locally than to send customer emails or invoices into someone else’s cloud. Others point out that without fine-tuning Laya is noticeably behind on accuracy and runs into the context limit, so for production here and now Jev looks more reliable, unless you bring the local model in as a temporary solution and fine-tune it on your own data over time.
Either way, the System One class had only one member not long ago, a closed one, and now there’s an open one too. You can look inside it and rework it for yourself, instead of just calling a third-party provider’s API blind.
Bottom line
Three days passed between the Jev and Laya releases. It’s quite possible more models of this class show up soon, maybe from big companies too. Curious to see where all of this goes.