Use Jev, a fast and cheap decision model, for large-scale data classification and clustering. Then, apply more expensive, powerful LLMs like Astra to these refined datasets for deep analysis. This hybrid approach dramatically reduces costs and unlocks complex data products that were previously cost-prohibitive.
Use Jev to perform pairwise comparisons on thousands of pull requests, asking "are these related?" to automatically form thematic clusters. A cheap LLM then labels these clusters (e.g., "tech debt," "new features"), providing a fast and accurate overview of engineering efforts for pennies.
Unlike standard LLMs that generate text, Jev is optimized for making choices from predefined options (e.g., yes/no, 1-10 scale, pick from a list). This makes it a "System 1" model, ideal for high-speed classification, routing, and filtering tasks that serve as smart "if" statements within larger applications.
Jev's pricing is fundamentally different, charging only for input tokens at a very low rate ($0.04/million) and not for its minimal output. This economic advantage makes it feasible to run analysis on huge, unstructured datasets—like millions of pairwise comparisons—for just a few dollars, a task previously cost-prohibitive.
Jev's extremely low latency allows it to be placed inside real-time application loops, a feat difficult for slower, generative LLMs. This unlocks novel user experiences, such as analyzing a user's voice sentiment live to change UI elements or playing a game by interpreting screen content without perceptible delay.