Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
JEV Speeds Up Agent Loops
Most AI agents are slow because they keep sending tiny routing decisions to a large model. JEV moves repeated decisions into a faster layer and keeps the heavy model for parts that need reasoning. Roadmap: Faster Agent Loop = Structured State + JEV.
Most AI agents are slow because they keep sending tiny routing decisions back to a large model JEV moves those repeated decisions into a faster layer and keeps the heavy model for the parts that actually need reasoning roadmap: Faster Agent Loop = Structured State + JEV
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Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
