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.
Why is Jev ~200x faster than an LLM judge?
Jev has no decode stage; the answer comes from a single prefill pass, making it about 200x faster than a traditional LLM judge.
Why is Jev ~200x faster than an LLM judge? Short answer: it has no decode stage. The answer comes out of a single prefill pass. 🧵
· 0 likesOpen on X
More like this
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.
