Jev é Tudo Isso Mesmo? Testei a Nova IA na Prática
Luís demonstrates evaluating marketing leads in n8n using Jev compared to traditional LLMs. He explains Jev's structured choice, score, and probability outputs, illustrating how CRM event histories can be classified with low latency and favorable per-request economics.
Original by Promovaweb Automação e IAIntegrationsIntermediate41 min 29 secPublished Source reviewed
Before you press play
What you’ll find in the video
Jev provides native structured output formats like choice, score, and boolean probabilities designed for software ingestion without output parsers.
In the author's n8n workflow test, Jev responded noticeably faster to large CRM JSON payloads than LLMs like GPT models.
The creator's comparative cost estimate showed Jev significantly reducing pricing per thousand requests despite consuming more tokens on raw input.
Worth knowing
Gemini-assisted video/transcript review. Latency, token counts, and cost metrics reflect the author's specific n8n/OpenRouter test payload and should not be taken as universal benchmarks.