On October 1, 2026, Michelle Chen, Alex Reneau and Kevin Flansburg announce on the Cloudflare blog two decision models, Clef and Clef-flash, hosted on Workers AI and published on Hugging Face under the Apache 2.0 license, along with a reinforcement-learning fine-tuning product. The post opens from the "buzz" around TypeSafe's Jev model: a decision model produced cheaply, quickly, and consistently returns bounded structured outputs, meant to be inserted into a workflow when a decision is required, as opposed to LLMs, which are largely non-deterministic but open-ended.
A decision model receives a state, for example a support message, and answers typed questions (urgency, team, severity) with probabilities, which the code uses to route, escalate, or hand off to a human. At Cloudflare, Clef classifies domains for Threat Intelligence: 2.2 seconds to fetch, render, and classify a site, versus 4.7 seconds for gpt-oss-120b, which returned only two categories.
Clef differs from Jev through a vision encoder, hence image classification, and a 64k context versus 32k. The API is compatible with Jev's, making replacement straightforward. On the Jev Decision Index, Clef leads; the tables show clear advantages (BFCL 98.47, BANKING77 94.20, CLINC150+OOS 97.43) and cases where Jev or a variant stay ahead (When2Call, BRIGHT, PhishNChips). On TypeSafe's workflow evaluations, Clef beats Jev on three of four. Median latency: 209.3 ms for Clef, 38.8 ms for Clef-flash, 524.1 ms for Jev; hosting on the edge GPU network reduces network latency. Clef can thus sit on an agent's critical path, ahead of an LLM tasked with acting.
Training: the project started from an adaptation of DiffusionGemma exposing logprobs, building on work by Matt Mastracci. Clef uses a frozen Qwen3.8-27B (Clef-flash: Qwen3.5-9B) for a prefill-only pass, then scores the schema's valid choices in parallel, without text generation. A two-stage attention routing head and rank-256 adapters are trained with label-smoothing cross-entropy and a Brier loss for calibration, then with RLCD, which grants partial credit to adjacent ordinal choices.
The fine-tuning service initially targets customers supported by forward-deployed engineers, with a self-service version to follow. It relies on AI Gateway (dataset), Workers AI (rollouts), Containers (scoring sandbox), a new Trainer (weight updates), and deployment of brought models. Cloudflare sees this as a way to specialize Clef (moderation, support triage, bots) using its fifteen years of labeled data, and to embody its "cloud agent."