Both models answer the same class of question: given some text, return a decision with a probability attached. They are not interchangeable, and the headline latency number hides most of the difference.
The short version
Laya is faster and open. Jev is more accurate out of the box. Which one you want depends on whether you have labelled data.
- Laya: Apache-2.0, self-hosted, ~33 ms median per decision on a T4, requires fine-tuning to be useful
- Jev: closed, hosted API, 236–276 ms per decision, usable immediately as shipped
Head-to-head on reported numbers
| Task | Laya | Jev | | --- | --- | --- | | Typed decisions | 0.766 | 0.727 | | Banking77 (77 options) | 0.425 | 0.870 | | Soft distribution match | 0.471 | 0.580 | | Calibration error (ECE) | 0.081 after temperature fitting | 0.246 | | Median latency (T4) | ~32.8 ms | 236–276 ms | | Cost | $0 self-hosted | per-token billing |
Where Laya wins
Latency and cost at volume
Roughly seven to eight times faster per decision, and batch throughput amortises further — reported figures land around 7.2 ms per question. If you are classifying a million items a month, that difference is the whole budget conversation.
Data residency
Self-hosting means the text never leaves your network. For regulated workloads this alone decides the question, regardless of accuracy.
Calibration, after work
After temperature fitting on your own data, calibration error drops into a range where the probability is genuinely informative. That is the property that makes automation safe.
Where Jev wins
Accuracy on day one
Laya's base checkpoints score close to random zero-shot. If you cannot invest in labelled data and a training loop, Jev is the only practical option of the two.
High-cardinality classification
On Banking77, Jev's 0.870 against Laya's 0.425 is not a rounding difference. The wider your option list, the worse Laya performs relative to Jev.
Soft distribution matching
If downstream logic consumes the full probability vector rather than the argmax, Jev's distribution is closer to the teacher — 0.580 vs 0.471.
A practical decision rule
If your option counts are small, your categories are stable and you have a few thousand labelled examples — self-host Laya. If you need something working this week, or your label space is wide, use a hosted API.
There is also a hybrid worth considering: prototype on a hosted API to get a baseline, then fine-tune Laya and compare against that baseline on your own evaluation set. Only switch when the numbers hold.
What to measure before committing
1. Accuracy on a held-out slice of *your* data, not a public benchmark 2. Calibration error after temperature fitting 3. p95 latency on your target hardware, not the median 4. Cost per 100k decisions including the training run