Jev is a model that processes states and typed questions to return structured JSON with probability distributions. It outperforms previous setups in speed and cost, making it ideal for routing and decision-making tasks.
added by @Al_Grigor
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Jev offers a new intelligent decision-making primitive that enhances model routing and classification tasks. It allows for quick and cost-effective decision-making, improving the efficiency of LLM calls in applications.
The Jev adapter classifies prompts before they reach the LLM, reducing token consumption and costs. It provides classification and confidence scores, marking prompts for review when confidence is near the threshold.
Jev, a new decision model, demonstrated a routing cost 125 times lower than GPT-5.5 when deciding actions for a coding agent after a failed test.
Jev was tested in an AI customer feedback system using 1,040 GitHub issues, proving to be 98% cheaper and 84% faster than Claude Sonnet 4.6 while maintaining higher accuracy.
TypeSafe AI's Jev model has been integrated into a system layer, providing clean, typed decisions from unstructured state in under 500ms. This integration enhances backend processes by scoring and filtering context efficiently.
The Hermes setup now features 443 unique skills, utilizing Jev for skill selection. Jev achieved 94.4% exploratory reused-data performance, ensuring confidence-aware decision-making.