Blog›Guides

Laya vs Jev: The Open-Source Answer to the Decision-Model Wave

Days after TypeSafe launched Jev, Convai Innovations shipped Laya: an Apache 2.0, 421M-parameter decision model claiming 33ms decisions across 100+ languages. How the two compare, honestly, including the fine-tuning caveat.

Laya vs Jev: The Open-Source Answer to the Decision-Model Wave

Last week the AI world met Jev, TypeSafe AI's non-chat "System One" model that returns typed decisions instead of prose. It took days for an open answer to appear: Laya, an Apache 2.0, open-weight decision model from Convai Innovations, a startup out of Kasaragod, Kerala, India. If Jev made the category interesting, Laya makes it accessible to everyone. Here is what it is, how the two compare, and what to watch out for.

What Laya is

Laya is a non-autoregressive decision model: it does not generate text at all. You hand it a piece of state (an email, a support ticket, an agent trace, a JSON object) plus typed questions with fixed answer sets, and it returns the chosen answers with calibrated probabilities in a single forward pass. No tokens, no prose, nothing to hallucinate, no JSON parsing failures.

The main English checkpoint is small by modern standards, roughly 421 million parameters on a ModernBERT-large backbone, which is exactly the point: it runs on modest local hardware, and a multilingual variant covers 100+ languages.

Laya vs Jev, honestly

| | Jev (TypeSafe AI) | Laya (Convai Innovations) |

|---|---|---|

| Access | Closed, hosted API | Apache 2.0 open weights, self-hostable |

| Latency (vendor-reported) | ~236-276 ms hosted | ~33 ms p50 on a single Tesla T4 |

| Cost | Per-call API pricing | $0 self-hosted |

| Languages | English-focused | 100+ |

| Out of the box | Tuned for decision tasks | Near-random until you fine-tune it |

Two big caveats keep this honest:

All the headline numbers are vendor benchmarks. Jev's "193x faster than Claude" came from TypeSafe's own evals; Laya's "7.8x faster than Jev" comes from Convai's. Independent verification is thin for both. Treat every number as a claim to test on your own workload.

Laya's accuracy requires fine-tuning. The project itself acknowledges the base model scores near random on decision tasks out of the box. Jev arrives task-ready via its API; Laya arrives as an efficient open architecture you adapt. That difference matters a lot depending on whether you have training data and ML muscle in-house.

Why this category matters

Both models bet on the same idea, named after Kahneman's System 1: most production "AI" work is not open-ended writing, it is millions of fast, narrow decisions - is this fraud, which queue, does this violate policy, what is the next step. A tiny typed classifier answers those cheaper, faster and more reliably than a chat model asked to "respond only in JSON."

The emerging stack looks like this:

Generative models (the ones you prompt from our catalog) for creation and reasoning

Decision models (Jev, Laya) for the routing, scoring and filtering around them

Which one should you try

You want zero-setup, task-ready decisions and accept API pricing: Jev.

You have data to fine-tune, want local/private inference, multilingual coverage, or zero marginal cost: Laya - the weights are on Hugging Face, and community runtimes already exist for Node.js/TypeScript via ONNX.

You are just prompting for images, video or text: neither replaces anything you do today - but if you are building automation around your generations (moderating outputs, routing requests, scoring results), this category is about to be the cheapest tool in your box.

Full technical breakdowns in our wiki: Laya and Jev.

Laya vs Jev: The Open-Source Answer to the Decision-Model Wave

Laya vs Jev: The Open-Source Answer to the Decision-Model Wave

What Laya is

Laya vs Jev, honestly

Why this category matters

Which one should you try

Related Articles