Compared on repository metadata · 20 September 2026
Jev vs laya-vision-smolvlm-256m
Both are decision models. What separates them is licence, architecture, size and whether anyone has checked the numbers. Only one of those is a matter of opinion.
Side by side
Rows marked with a dot are where the two genuinely differ. Everything here comes from each repository’s own metadata, read on 20 September 2026.
| Dimension | Jev | laya-vision-smolvlm-256m |
|---|---|---|
| Published by | TypeSafe | thaitea |
| Weights | Hosted only | Open |
| Licence | Proprietary | cc-by-nc-sa-4.0 |
| Base model | Not disclosed | HuggingFaceTB/SmolVLM-256M-Instruct |
| Architecture | Not disclosed | Decoder |
| Parameters | Not disclosed | 237M |
| Local formats | Original weights only | Original weights only |
| Measured by us | Yes | No |
| Attention | Not on Hugging Face | 11 likes |
What actually separates them
One you run, one you call
Jev is a hosted API: no weights to operate, but a key, a network round trip and a vendor version number in the path of every decision. laya-vision-smolvlm-256m is yours to run, which removes all three and hands you the operational cost instead.
One has been measured and one has not
We ran Jev on a frozen 2,000-decision split and published accuracy, calibration and a risk and coverage curve. Nobody outside its own authors has published anything comparable for laya-vision-smolvlm-256m. That is a gap in the evidence, not a verdict about the model.
What this page cannot tell you
Which one is more accurate on your traffic. Nothing above is a benchmark result, because for laya-vision-smolvlm-256m no independent benchmark exists. A comparison built from metadata narrows a shortlist. It does not close it. That takes your own data.
Find out what your data actually supports
We turn your historical decisions into a frozen test set, then tell you which model, which threshold, and how much of it you can safely automate.