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.
Comparing Jev's Core Idea with Earlier CUA Work
The author notes Jev is trending today, but sees little difference between its core idea of optimizing the correct token over candidate outputs and their attempt two years ago with a collaborator to maximize similarity between CUA states and actions, now included in MMEB-v3.
#Jev is viral today, but I don’t see much difference between its core idea of optimizing the correct token over candidate outputs (tell me if wrong) and our attempt two years ago with @DehanKong285793 to maximize the similarity between CUA states and actions. Now it's in MMEB-v3.
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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.
