Cloudflare Tries to Outplay Jev With Open-Weight Clef Models

Cloudflare has launched two open-weight decision models, Clef and Clef-flash, designed for structured yes/no, multiple-choice, and ranking tasks while also supporting images, video, and up to a 64K context window. Cloudflare says its models outperform TypeSafe's Jev on several benchmarks and can run locally from Hugging Face. The Register reports: For starters, Clef has an LLM backbone. According to Cloudflare, Clef uses specially post-trained, frozen versions of Qwen3.8-27B and Qwen3.5-9B for Clef and Clef-flash, respectively, with the Qwen backbone performing a prefill-only pass during inference. Clef is still fast - faster than Jev, to be fair - and scores choices in parallel after that prefill-only pass. It's not clear what Jev's underlying architecture is, as TypeSafe has kept that a secret.

As for its speed and capability, Clef moves fast. Cloudflare ran it against Jev and some other open decision models using the Jev Decision Index available on Hugging Face, and the company's own ranking suggests Clef is slightly slower than other open models, but more accurate, with Clef-flash just as accurate as most of the others, but far faster.