Laya vs. Jev vs. Parallel Decisions: open weights, hosted, or web-grounded

Laya, Jev, and Parallel’s Decisions API are positioned for different decision-making tasks: Laya is an open-weight, self-hosted encoder optimized for fast, high-volume classification or routing over supplied text, especially after fine-tuning; Jev is a hosted TypeSafe model designed for stronger zero-shot decisions over long inputs and up to 255 options; and Parallel retrieves live web evidence and citations for questions that cannot be answered from the provided material. Laya is free to run on owned hardware, supports multilingual use, and can emulate Jev’s API, but its base models perform poorly zero-shot and are constrained by shorter context windows and accuracy degradation with many choices, while its favorable benchmark result against Jev came from a checkpoint trained on that benchmark’s data. Jev costs $0.042 per million input tokens, supports up to 64K-token requests, and is presented as preferable for long documents or many-category decisions without customer-specific training. Parallel costs $1 per 1,000 requests, supports up to 100 questions per request, and is slower and more expensive for answers already present in input text because web search is included, but it is suited to current external facts that require audit-ready sources. The comparison recommends combining a local or hosted routing model with web retrieval for fact verification, while cautioning that decision-model probability outputs can fail on numeric comparisons and should be checked in code or reviewed by people.

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