Cloudflare Clef: Choosing and Testing a Decision Model

Cloudflare released the open-weight Apache 2.0 Clef family on Workers AI on October 1, 2026, consisting of the 27B-parameter Clef and faster 9B Clef-flash, which return calibrated probabilities for typed yes/no, choice, and score decisions rather than generating free-form text. Designed to be API-compatible with TypeSafe AI’s Jev and to support text, JSON, images, and video, the models target bounded agent tasks such as ticket routing, API selection, domain classification, and hallucination checks. Clef-flash has the lowest reported median latency at 38.8 ms, versus 209.3 ms for Clef and 524.1 ms for Jev, and performs strongly on tool-calling benchmarks, but it trails Clef substantially on out-of-scope intent detection and hallucination detection. Clef generally leads the family on guardrail-style classification tasks, while Jev performs better on reasoning-intensive benchmarks such as GPQA Diamond, MMLU-Pro, BBH, and decisions about whether to call a tool. Cloudflare attributes Clef’s speed to a non-autoregressive prefill-and-scoring architecture with frozen Qwen backbones, a schema head, and low-rank adapters, while emphasizing probability calibration and robustness to schema order. The text recommends evaluating either model on labeled, domain-specific data before deployment, including rare classes, out-of-scope inputs, calibration, long-context truncation, wording and ordering variation, tail latency, and the downstream actions an agent takes from model outputs.

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