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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.
Sean Goedecke explains how to build real-time AI systems using "System One" models—fast classifiers that pick from multiple-choice options instead of generating arbitrary text. He shares two practical techniques: tiered goals (breaking decisions into hierarchical loops) and tournament sampling (narrowing large option sets through multiple passes).
- You can convert any LLM into a fast classifier by batching single-token outputs with structured choices, getting predictable inference timing without retraining the model
- Tiered goal hierarchies (strategic → tactical → specific targets) let System One models handle complex tasks like game-playing by providing short-term context, similar to how LLM reasoning chains work
- Tournament sampling outperforms absolute scoring for large option sets—the Wikiracing demo found the optimal 3-link Wikipedia path by sampling 100 links at a time rather than trying to rank all 1,000+ options at once