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Tested Jev to see what the buzz is all about and this is what I found for my generative websites case 🧵 TL;DR: it's not a chatbot. It's a "choice machine." And for the right job, it's stupidly fast, cheap, and accurate No free text generation, ever.

It only answers 3 question types: 🅰️ Choice (pick 1 of N) ✅ Noul (yes/no, 0-1 confidence) 🔢 Score (numeric rating)

My site has a generative pipeline with 2 stages: 1️⃣ Classify user intent + extract entities 2️⃣ Pick which UI blocks to render (hero, comparison table, testimonials, etc.) Both are "decision" tasks, not "writing" tasks. Perfect test case for Jev

Block-selection results: 15/15 (100%) ✅ I turned "which blocks should render?" into 21 parallel yes/no questions (one per block type), answered in a SINGLE API call, then combined with plain code logic. Matched Claude Sonnet 4's accuracy at ~4.4x the speed.

Intent classification: also 15/15 (100%) tweaking the process. Instead of generating the response text make Jev answer if product X was mentioned (yes/no) The one real limit I hit: fully open-ended extraction (arbitrary "use cases"/"features" with no fixed list). Jev genuinely can't do that

💰 Cost check: ran it against ~28 other providers (Bedrock Claude/Nova/Llama, Cloudflare Workers AI, self-hosted models). Jev landed in the CHEAPEST tier on par with micro-models, while being the fastest 100% on block selection. ~80-400x cheaper than Claude Sonnet/Opus for equivalent accuracy.

Not a chatbot replacement, a decision-engine replacement. Cheaper, faster, and often just as accurate, for that slice of the pipeline.

My generative websites demo bsky.app/profile/csan...