Jev 1.13 Targets Fast, Typed Decisions

Jev’s strongest idea is architectural: reserve expensive generative models for reasoning and writing, then use a specialized decision layer for repetitive judgments.

• –Typed outputs reduce parsing failures and constrain responses to application-defined choices
• –At $0.042 per million input tokens with free output, it is practical for high-volume classification and validation
• –Confidence scores enable thresholding, human review, or escalation to a stronger model
• –The model complements LLMs rather than replacing them; it cannot generate prose, code, or complex plans
• –Developers should evaluate calibration on their own data before treating confidence as correctness