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# Cloudflare releases Clef, open-weight decision models, and an RL fine-tuning service

**[Collections](https://daily.dev/sources/collections)** · 2 min read · 1 upvotes · 0 comments

## Summary

Cloudflare has released Clef and Clef-flash, open-weight decision models hosted on Workers AI, designed for fast, deterministic structured classification used in routing and gating decisions within agentic workflows. Built on a Qwen backbone with a non-autoregressive, prefill-only scoring approach, Cloudflare claims the models beat Typesafe AI's Jev model and general-purpose LLMs like gpt-oss-120b on accuracy and latency, though these are self-reported benchmarks. The models are Jev-API compatible, Apache 2.0 licensed, and downloadable from Hugging Face. Cloudflare also launched a reinforcement learning fine-tuning service, starting with forward-deployed engineers before a self-serve platform arrives, built on AI Gateway, Containers, and a new Trainer component. LangChain's Harrison Chase praised the open-weight approach, arguing decision models should be as swappable as the main model in an agent harness.

## Content

Cloudflare has released two decision models, Clef and Clef-flash, trained by its Workers AI team. Both are hosted on Workers AI, and the weights are on Hugging Face under Apache 2.0.

## What they do

Decision models handle fast, deterministic structured classification, the kind of small routing and labeling steps that agentic workflows run constantly. Clef is built on a Qwen backbone. It doesn't generate text token by token. It uses a non-autoregressive, prefill-only scoring approach, which is where the speed comes from.

Cloudflare says Clef beats Typesafe AI's Jev model and general-purpose LLMs like gpt-oss-120b on accuracy and latency across dozens of benchmarks. Those are Cloudflare's own numbers. The models are also Jev-API compatible, so existing Jev integrations should be easy to move over.

## Running it locally

The open weights are already getting used. @lucataco, in a post that @kentcdodds reposted, ran clef-flash locally on an M5 Max and noted that it can handle images. Georgi Gerganov's llama.cpp v0.6.0 release lists Clef support for both text and vision, per a repost from @antirez.

## RL fine-tuning

Cloudflare is also launching a reinforcement learning fine-tuning service. It starts with a hands-on team of forward-deployed engineers. A self-serve platform comes later, built on AI Gateway, Containers, and a new Trainer component that redeploys fine-tuned models on Workers AI.

## Reaction

Developers jumped on it quickly. @jamesqquick said Clef landed the week after Jev, and that he plans to use it in his recipe app to map unstructured recipe data to structured ingredients. @steipete said he'd never seen an idea spread so fast. @hwchase17 called it "decision model season" and credited @ritakozlov and the Cloudflare team for the open weights. His view: a harness should be able to swap its decision model as easily as its main model. I think that's the right bar.

InfoQ's Renato Losio also covered the release.

## Questions this post answers

### What is Cloudflare's Clef model and what is it used for?

Clef and Clef-flash are open-weight decision models released by Cloudflare on Workers AI, built on a Qwen backbone using a non-autoregressive, prefill-only scoring approach instead of token-by-token generation. They are designed for fast, deterministic structured classification, such as the routing and gating decisions agentic workflows make constantly. They are Apache 2.0 licensed, Jev-API compatible, and downloadable from Hugging Face.

_Developers wiring decision models into agent harnesses can follow releases like Clef on daily.dev._

### Is Cloudflare's Clef model faster than gpt-oss-120b for classification tasks?

Cloudflare claims Clef and Clef-flash deliver higher accuracy and lower latency than general-purpose LLMs such as gpt-oss-120b, as well as Typesafe AI's Jev model, across dozens of benchmarks. These figures come from Cloudflare's own testing rather than independent verification, so they should be treated as vendor-reported claims pending outside validation.

_Anyone comparing decision models for routing calls can track independent benchmarks like this on daily.dev._

### What is Cloudflare's new reinforcement learning fine-tuning service?

Cloudflare launched an RL fine-tuning service that initially works through a hands-on team of forward-deployed engineers, with a self-serve platform planned for later. The self-serve version will be built on AI Gateway, Containers, and a new Trainer component that redeploys fine-tuned models directly onto Workers AI.

_Teams planning to fine-tune their own agent models can follow services like this as they evolve on daily.dev._

## Community take

How the wider developer community reacted, aggregated from 1 discussion and 2 comments across x (as of 2026-10-08).

**TL;DR:** The small sample of replies is enthusiastic and curious, with developers expressing interest in trying out Clef for their own tooling.

**Sentiment:** 70% positive · 30% mixed · 0% skeptical

**The case for**

- People are eager to integrate Clef into existing projects, like an orchestrator CLI used with Claude and Codex.

**By community**

- x (positive): The few replies express excitement and interest in adopting the new models for personal projects.

**Highlights**

> @jamesqquick I've been wanting to try this out. I've been making an oss Jev orchestrator cli to use with Claude and Codex.  But now I guess I'm going to have to branch out
> — [PeteCapeCod on x](https://x.com/PeteCapeCod/status/2107943027323105509)

> @jamesqquick Love this
> — [fayazara on x](https://x.com/fayazara/status/2107916934381785279)

**Source threads**

- [x](https://x.com/jamesqquick/status/2107916182263316799) · 0 points · 2 comments

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---

Tags: [#open-source](https://daily.dev/tags/open-source), [#ai-agents](https://daily.dev/tags/ai-agents), [#cloudflare](https://daily.dev/tags/cloudflare), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

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