Cloudflare's Clef models make decisions instead of writing text

On 1 October Cloudflare released two open-weight "decision models": Clef (27B, built on Qwen3.8-27B) and Clef-flash (9B, built on Qwen3.5-9B). They are the first models its Workers AI team has trained itself.

They don't generate text. You give them a state (text, JSON, images or video) and a schema of typed questions, such as multiple choice, true or false, ranked or scored. In a single pass they return a calibrated probability for every allowed answer. Route the ticket, block the request, escalate to a human.

The details

• weights are Apache 2.0 on Hugging Face; training data and pipeline are not published, so this is open weights, not fully open source
• hosted on Workers AI at $0.24 and $0.09 per million input tokens, with no output charge
• 64K context, with Qwen's vision encoder kept
• Cloudflare also announced a reinforcement-learning fine-tuning service

The caveat

All benchmark numbers are Cloudflare's own and unreplicated. One independent beta measurement put Clef-flash at about 661 ms, against the 38.8 ms Cloudflare claims.

Why it matters

An agent makes thousands of small routing and gating calls, and doing them with a general chatbot model is slow, unpredictable and expensive. An agent doesn't want a monologue; it wants an answer it can act on. Clef is a bet that agent stacks get built around that.