Model fit · JevBench v1.3.0

Which Laya checkpoint matches your workload?

The choice depends on the exact checkpoint, language, input length, and who will operate inference.

Laya is a family of open checkpoints with English, multilingual, and typed-decision variants. Jev is designed to answer typed decisions without a fine-tuning run.

Test your workflow in Playground Evidence that changes the decision

Evidence that changes the decision

Treat the benchmark as a starting point, not a verdict

The JevBench row is one evaluated configuration: Laya English ModernBERT-large (421M), run on CPU with a 512-token budget. Other Laya checkpoints need their own evaluation.

Hard-tier accuracy · JevBench configuration

Jev

74.1%

Laya

34.1%

This is the English 421M checkpoint result, not a score for every Laya variant.

Context budget

Jev

64k tokens

Laya

512–1,024 tokens; some variants support up to 8k

The tested English checkpoint used 512 tokens; check the selected variant before sizing inputs.

Deployment control

Jev

Hosted API

Laya

Open weights

Local execution is an important advantage when data must stay in your network.

Choose by the state of your dataset

The same model can be a poor fit at one project stage and a strong one later.

The label set is still changing and examples are scarce.

Jev fits the first pilot

Define questions at request time and get a zero-shot baseline before committing to a training dataset.

You have labeled examples and a stable, narrow task.

Evaluate the matching Laya checkpoint

Open checkpoints let your team select a language or typed-decision variant and keep inference inside its own environment.

The workflow is multilingual or has a strict data boundary.

Laya may be the better fit

The mmBERT checkpoint targets 100+ languages and can run in your environment; verify quality per language on your data.

Build and ownership

Compare the work required before the first reliable decision.

Jev · ready-to-call API

Establish a baseline without a training pipeline

The shorter path is useful while the product team is still learning which labels and edge cases matter.

No labeled dataset or GPU deployment is needed to begin testing.
The request can carry a longer shared state and several typed questions.

Laya · open weights

Choose a checkpoint around language and input needs

Self-hosting can make sense at scale, but the team takes on checkpoint selection, evaluation, calibration, monitoring, and serving.

English ModernBERT-large (421M) uses a 512-token context; multilingual mmBERT (322M) and typed-decisions ModernBERT-large (421M) use 1,024 tokens, with some variants supporting up to 8k.
Published Tesla T4 latency is hardware-specific; CPU and hosted-network numbers are not interchangeable.

Deployment checklist

Make these constraints explicit in your bake-off.

Training: JevDesigned for zero-shot typed decisions; LayaChoose among English, multilingual, and typed-decision variants
Languages: JevStrongest benchmark results are in English; LayammBERT checkpoint targets 100+ languages
Long inputs: Jev64k tokens per request; LayaVariant-specific: 512–1,024 tokens, with some supporting up to 8k
Operations: JevHosted API and usage credits; LayaApache-2.0 weights; your team owns inference

Sources and scope

The JevBench Laya row refers to the English ModernBERT-large 421M checkpoint evaluated on CPU with a 512-token budget. Laya currently publishes multiple checkpoints with different language and context capabilities; benchmark the exact checkpoint and hardware you plan to deploy.

JevBench model board Laya project Laya source code

Test your workflow in Playground