# Laya vs Jev: open weights or a hosted API > Searching Jev vs Laya? Laya is open weights you run yourself. Jev is the hosted decision API. Latency, cost, privacy, and when using both is the sane move. ## Answer Pick Laya when you need open weights on your own machines. Pick hosted Jev when you want a managed decision API and gateway routing. Using both is normal. Same typed primitives, different deployment. Read this before you rip out a working Jev integration on a Friday afternoon. ## What both products do Laya and Jev answer the same shape of question: pick a label, score something, or return a probability (noul) over JSON or text state. Neither is a chat model. They will not draft your apology email. That is why teams compare them to System One-style gates instead of to GPT-class generators. For Jev vocabulary, start with /learn/jev-typesafe and /learn/system-one. For Laya architecture, see what-is-laya in this hub. ## Comparison at a glance Access: Laya publishes Apache 2.0 checkpoints on Hugging Face (English, multilingual, typed-decisions fine-tune). Jev is API-only via TypeSafe and partners such as Vercel AI Gateway. Weights: Laya is open. Jev weights are not downloadable for self-host. Latency: Convai reports about 33-40 ms per question on a Tesla T4 for Laya. Laya-MLX reports about 7-16 ms P50 for short inputs on an M3 Max (see laya-mlx-apple-silicon). Hosted Jev p50 is often quoted in the hundreds of milliseconds end-to-end in third-party posts. Do not compare those without reading benchmarks-and-fairness. Context: English Laya defaults to 512 tokens with a split between state and option head budget. Multilingual and typed-decisions checkpoints can go toward 1k-8k encoder context with tuning. Jev publishes its own limits in TypeSafe docs. Verify there instead of assuming parity. Cost: Laya is mostly your GPUs and engineers. Jev is metered API pricing. Fine-tuning: Laya expects you to specialize checkpoints. Jev improvements ship as hosted model versions. Privacy: Laya can run air-gapped after download. Jev sends state to TypeSafe unless you have a private contract. Ops: Laya means Router preload, VRAM, calibration, and checkpoint updates. Jev means keys, quotas, and gateway config. ## Accuracy and calibration Convai's public card reports higher argmax accuracy on some public slices for routed Laya than published Jev 1.13.0 figures. It also notes Jev leads on soft distribution matching and high-cardinality choice sets (for example Banking77-style dozens of labels at default token budgets). Raw expected calibration error differs before temperature fitting. Laya documents post-hoc temperature scaling to reach lower ECE on their evals. Argmax accuracy and calibrated probabilities are not the same KPI. Moderation pipelines care about the latter. Do not treat vendor comparison tables as independent audits. ## When to choose Laya Choose Laya when data must stay on device or inside a VPC you control, when you want to fork or fine-tune weights, when you already run GPU inference, or when sub-50 ms local decisions matter more than zero ops. The typed-decisions checkpoint targets invoice, security, and support-triage style workflows Convai fine-tuned. Base English and multilingual checkpoints are not magic on those tasks without specialization. ## When to choose Jev Choose Jev when you want a supported HTTP API, predictable billing, gateway integrations (see /learn/vercel-ai-gateway), and large option sets without tuning head_max_len yourself. Teams already on TypeSafe SDKs, MCP servers, and agent skills often stay on the hosted path unless privacy or unit economics force self-host. ## When to use both A practical pattern is local Laya for high-volume pre-filters or game ticks and hosted Jev for escalation, audit-heavy workflows, or option sets you do not want to maintain weights for. See migration-and-coexistence and the showcase snake demo for local loop speed: /showcase?demo=laya-mlx-lonely-mh. ## In the directory - [Laya vs Jev hub](https://jev.aitools.fyi/learn/laya-vs-jev) - [System One primer](https://jev.aitools.fyi/learn/system-one) - [What Jev is](https://jev.aitools.fyi/learn/jev-typesafe) - [Benchmarks](https://jev.aitools.fyi/categories/benchmarks) - [SDKs and clients](https://jev.aitools.fyi/categories/sdks) - [@receptron/laya](https://jev.aitools.fyi/tools/receptron-laya) - [ollaya](https://jev.aitools.fyi/tools/ollaya-dev-ollaya) - [Laya-CoreML](https://jev.aitools.fyi/tools/mizorewww-laya-coreml) ## FAQ ### Is Laya faster than Jev? Often on a warm local GPU, yes in vendor and community benchmarks. That is still not the same measurement as your production Jev path through TLS, auth, and region. Size SLAs using your own traces. ### Which has better accuracy? It depends on task, language, option count, and whether you fine-tune Laya. Public tables mix benchmarks. Jev leads on some high-cardinality sets while Laya's typed-decisions checkpoint leads on Convai's typed workflow eval. See benchmarks-and-fairness. ## Sources Directory copy checked 2026-09-30. Primary sources: - [Hugging Face: convaiinnovations/laya (model card and benchmarks)](https://huggingface.co/convaiinnovations/laya?ref=jev.aitools.fyi) - [GitHub: NandhaKishorM/laya (upstream SDK)](https://github.com/NandhaKishorM/laya?ref=jev.aitools.fyi) - [PyPI: laya package](https://pypi.org/project/laya/?ref=jev.aitools.fyi) - [TypeSafe docs: System One / Jev API](https://docs.typesafe.ai/?ref=jev.aitools.fyi) - [Laya-MLX: BENCHMARKS.md (M3 Max, independent port)](https://github.com/mizorewww/laya-mlx/blob/main/BENCHMARKS.md?ref=jev.aitools.fyi) Canonical: https://jev.aitools.fyi/learn/laya-vs-jev/compare Markdown: https://jev.aitools.fyi/learn/laya-vs-jev/compare.md