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 vs Classifiers: No Training, Fast Inference
Jev isn't a new concept but builds on decades-old classifier interfaces, yet works for any problem without training and offers fast inference.
Unlike LLMs, Jev didn't invent a new concept. Classifiers have existed for decades, with exactly the same interface → ask a question, get a probability score. BUT, here's what the hype is all about: - it works for *any* problem, no training required - it's fast → you can do
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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.
