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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.
Cloudflare's Clef cloned Jev's LLM-based multiple-choice solver in two weeks and beat it 4x on speed with 2x better accuracy using a smarter inference approach. The post argues this shows how hard it is to build defensible software products without proprietary data.
- Clef uses a frozen Qwen model with parallel schema scoring instead of generating text, achieving 4x faster performance and 2x accuracy improvements over Jev on some benchmarks
- Open-sourcing Clef immediately eliminated any competitive advantage Jev had from being first to market
- Without proprietary data or a clear technical moat, launching novel LLM applications offers almost no protection against rapid cloning by better-resourced competitors