2 links tagged with all of: jev + classification + structured outputs
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Cloudflare open-sourced two decision models called Clef and Clef-flash that classify inputs into typed, structured outputs with probabilities — useful for routing customer support tickets, identifying phishing domains, or triggering automated agent actions. The models outperform Typesafe's Jev system on speed and benchmarks while supporting images and a 64k context window.
- Clef processes decisions 2x faster than general LLMs (2.2s vs 4.7s in Cloudflare's threat intelligence workflow) because it scores schema choices in parallel rather than generating text token-by-token.
- The models beat Jev on most Jev Decision Index benchmarks and Typesafe's own eval suite, with Clef-flash offering exceptional speed-to-quality tradeoffs; median latency is 209ms for Clef and 38.8ms for Clef-flash.
- Both models are open-sourced on Hugging Face under Apache 2.0, hosted on Workers AI at Cloudflare's edge for low network latency, and fully API-compatible with Jev for easy switching.
TypeSafe AI released Jev, a model that takes text input but outputs confidence scores and probability distributions instead of generated text. It's designed for classification tasks like spam detection and ranking, costs $0.042 per million input tokens with free output, and has already spawned open-source recreations and creative experiments.
- Jev returns structured probabilistic outputs (yes/no scores, rankings, category choices) rather than text, making it fast and cheap—you only pay for input tokens, not output.
- The model trades interpretability for simplicity: you get a floating-point score but no explanation of which signals drove the decision, raising serious concerns about hidden bias in high-stakes use cases like hiring.
- The community has already built unconventional projects with it (a chat model, a game player, a left-pad implementation) and created open-weight alternatives like Kev within days of release.