JEV COMPARISON · JEVBENCH V1.3.0 · UPDATED SEPTEMBER 22, 2026
Jev vs Laya
A model you fine-tune on your data, against one that works out of the box.
On this site
Jev 1.13.0
TypeSafe · System One model
Answers on day one, with no labelled data and no training run.
Alternative
Laya
Convai Innovations · Apache-2.0 421M decision head
Open weights, tens of milliseconds, and one of the cheapest systems to run.
The benchmark
JevBench v1.3.0, measured the same way
One benchmark ran all 52 systems under one method, so these bars are comparable with each other in a way that vendor-published figures are not. The composite combines intelligence, calibration, speed, and cost.
Composite score
Higher is better
JevBench v1.3.0
The top three finish within 1.4 points of each other, and then the board falls away sharply. The composite hides where systems actually differ, so the two charts below break it apart.
Accuracy by difficulty
Where the decisions separate
Easy
72 straightforward cases
Standard
96 everyday cases
Judge
146 evaluation-style calls
HardHard
220 genuinely ambiguous cases
Easy and standard decisions separate almost nothing. The hard tier is where these systems stop agreeing.
The missing fourth axis: cost
The composite also weighs cost, scored from each system’s public list price. Laya is one of the cheapest systems to run, while Jev trades per-call credits for a hosted, ready-to-use model with no idle GPU to operate.
See Jev pricingThe board
Every system on the board
The capability scores are charted above; this is the ranking and the deployment detail behind them. Jev and Laya are highlighted.
JevBench v1.3.0 scored 52 systems on the same 534 decisions (72 easy, 96 standard, 146 judge, 220 hard) on September 21, 2026, one request at a time.
Feature by feature
Jev and Laya side by side
What each is better at
The practical trade-off
Laya wins on ownership, speed, multilingual coverage, and self-hosting economics. Jev wins on zero-shot quality, long context, and getting a reliable decision path running today.
Where Jev wins
- Works zero-shot, with no labelled data or training run.
- Hard-tier accuracy: 74.1% against Laya’s 34.1%.
- 64k context per request for long tickets, documents, and traces.
- No small fixed ceiling on the number of choice options.
Where Laya wins
- Tens of milliseconds on a Tesla T4, with no network hop when self-hosted.
- 100+ language coverage from the multilingual checkpoint.
- Apache-2.0 weights that can be fine-tuned and run offline.
- Very low marginal cost once a busy GPU is already paid for.
The analysis
Two good fits for different constraints
Convai publishes strong latency and calibration numbers for Laya: 32.8ms on a Tesla T4 for the multilingual checkpoint, with 0.081 expected calibration error after temperature fitting. That is a compelling shape for high-volume, stable workflows.
The catch is equally important. Base checkpoints are close to random on unseen typed decisions and below a majority-class baseline. Laya becomes compelling when you have labelled examples, a stable label set, and the ability to fit the model to your task.
JevBench measured the untuned package on CPU and found 34.1% on hard decisions against Jev’s 74.1%. Long hard-tier states can also be truncated by Laya’s 512-token limit, while Jev accepts 64k tokens per request.
Data boundary
Laya stays inside your network; Jev sends requests to a hosted API.
Operating model
Laya trades API spend for GPU ownership, serving, monitoring, and calibration.
Decision shape
Both are strongest when software needs a typed choice, score, or yes/no judgment.
Language reach
Laya’s multilingual checkpoint is the clear choice when English is not enough.
Open implementation
What Laya actually is
Laya is a decision head over a frozen encoder. It scores the options you provide at request time instead of generating a sentence, parsing JSON, and validating it afterward. The main English checkpoint has 421M parameters; the multilingual checkpoint has 322M and covers more than 100 languages.
It is aimed at ticket routing, moderation, guardrails, invoice processing, and agent-trace observability: the classification layer around an LLM stack rather than the LLM itself.
Read Laya on GitHubSelf-hosted
Run the weights on your own GPU or CPU, with no per-call API bill.
Multilingual
Route more than 100 languages through the mmBERT checkpoint.
Direct probabilities
Return typed option probabilities without an autoregressive output loop.
Private by design
Keep state and decision criteria inside your network when required.
Laya is an independent open-source project and is not affiliated with or endorsed by TypeSafe or Jev AI.
So which should you use?
Choose around your constraints
Pick Jev if
- You do not have labelled data yet, which is where almost every project starts.
- Questions are defined at request time and change as you learn what the task is.
- Inputs are long, or a choice has more than a couple of dozen options.
Pick Laya if
- You have labelled data, a stable label set, and the capacity to fine-tune locally.
- You need non-English coverage or strict data residency.
- Volume is high and the decision is narrow enough for self-hosted inference to pay off.
The honest comparison
Try Jev on the cases you are actually arguing about.
A scoreboard is useful for orientation. Your own awkward cases are the better test.
FAQ
Jev vs Laya FAQ
Is Laya a drop-in replacement for Jev?+
No. They answer the same three question types, but Laya’s base checkpoints expect fine-tuning on your task. Jev is designed to work without a training run.
Is Laya really 7.8x faster than Jev?+
On Convai’s hardware, yes: 32.8ms on a Tesla T4 versus 236–276ms cited for Jev. JevBench measured Laya on CPU, so its comparable raw latency is slower. Hardware and network placement explain most of the gap.
Does Laya support other languages?+
Yes. The 322M mmBERT-base checkpoint covers more than 100 languages and is a real advantage when English is not your primary language.
How much does Laya cost?+
The weights are Apache-2.0 and free to self-host. JevBench estimates about $0.0029 per 1,000 decisions in compute, plus whatever it costs you to run and maintain the service.
Can Laya handle a long document?+
Not in one piece. Laya uses a 512-token budget per question, and JevBench noted that long hard-tier states were truncated. Jev accepts 64k tokens per request.
Laya and every other product named on this page belongs to its respective owner and is not affiliated with Jev AI. Figures are quoted from the public sources above as read on September 22, 2026 and can change as models and benchmarks evolve.