Cloudflare launches Clef, an open-source multimodal decision model built on Qwen

Cloudflare has released Clef, an open-source multimodal decision model family built on Qwen, according to a report from IT之家 carried by ifeng on October 2. The launch, which took place on October 1 local time, includes two models, Clef and Clef-flash. The “decision” framing refers to the ability to judge and classify input states rather than to generate conversational text.

Cloudflare says Clef leads in the Jev Decision Index benchmark, evaluated under Decision Index 0.2.1. Both models are fully compatible with Jev-API, which lets developers test and verify them directly and gives existing applications built on Jev a comparatively smooth migration path.

On base models, Clef is built on Qwen3.8-27B, while the lighter Clef-flash uses Qwen3.5-9B. That pairing gives the family both a precision-oriented option and a version aimed at cost- and latency-sensitive workloads.

The main difference from the existing Jev is multimodality. Clef ships with a native vision encoder that can parse image inputs directly and classify visual content, whereas the current Jev supports text-only classification. That means Clef can take tasks with images built in rather than relying on a separate image-understanding step.

The context window has grown as well. Clef extends context to 64k, compared with 32k for Jev, allowing developers to load richer system input states for more comprehensive judgments.

Alongside the models, Cloudflare released a new reinforcement learning product. With it, customers can fine-tune Clef for their own business scenarios, aligning a general decision capability with their own rules.

Cloudflare says Clef offers high accuracy and strong overall performance, holding its own against or surpassing comparable models across several benchmarks. The specific comparisons rest on the benchmark table the company published, and real-world behaviour will still need to be validated against production workloads.

The choice to build on Qwen and ship open source continues a trend of pairing decision models with open ecosystems. For enterprises, the value of such models lies less in raw benchmark scores than in whether they can be embedded cheaply into existing content moderation, risk control or classification pipelines. What to watch next: the open-source licence, real deployment costs, and how useful the reinforcement learning fine-tuning tool proves in customer settings.