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AI models

Cloudflare’s Open-Weight Clef Models Take Aim at Jev

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Cloudflare’s Clef and Clef-flash are open-weight decision models that return probabilities for answers defined in advance. Cloudflare says they follow TypeSafe AI’s Jev System One API and publishes benchmark and latency figures comparing the models—but those results are vendor-reported, not independently reproduced in the sources available here.

What Clef does—and how it differs from a general language model

A decision model takes an input, such as a support message, and answers typed questions using a fixed set of allowed responses. Instead of generating free-form text that software must interpret, it returns probabilities that can help an application route a request, assign a category, or escalate a case.

Cloudflare documents three question types: noul for yes/no questions, choice to select an option, and score to assess an input against an ordered rubric. Examples include identifying a support team and estimating severity. A request can contain up to 64 questions, according to Cloudflare’s changelog.

This setup is useful when an application needs a constrained decision rather than an open-ended response. It does not by itself establish that a model’s probabilities are calibrated for every deployment; teams should assess the outputs against their own data and decision criteria.

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Clef vs. Clef-flash: size and intended use

Cloudflare lists Clef as a 27B model and Clef-flash as a 9B model. Both have a listed 64K-token context window. Cloudflare positions Clef for decisions where precision is the priority and Clef-flash for latency-sensitive paths. The benchmark figures below show why it is worth comparing performance on the task that matters, rather than assuming either variant is universally better.

What Cloudflare’s benchmark comparison with Jev shows

Cloudflare says one of its Clef models ranked highest on seven of ten decision benchmarks. Its changelog reports these results for four highlighted tasks:

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Benchmark Metric Clef Clef-flash Jev
BFCL Case exact 98.47 98.76 95.75
BANKING77 Macro-F1 94.20 90.93 79.74
CLINC150+OOS Macro-F1 97.43 66.77 89.27
Home appliances Case exact 82.95 97.73 52.27

These are Cloudflare-published figures, not results independently reproduced in the cited sources. The split is also meaningful: Clef-flash leads the listed BFCL and home-appliances results, while Clef has the higher listed scores on BANKING77 and CLINC150+OOS. Benchmark rankings therefore offer a starting point, not an across-the-board verdict about which model will work better in a particular product.

TypeSafe AI’s September 15, 2026 Jev announcement describes the company’s own workflow evaluation approach and acknowledges that evaluation-team authorship may introduce bias. As with Cloudflare’s results, treat a model maker’s reported findings as attributed evidence rather than independent confirmation.

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Cloudflare’s reported latency figures

Cloudflare reports latency measurements across 43 benchmark runs. The published figures are:

Model Median latency p95 latency
Clef 209.3 ms 238.6 ms
Clef-flash 38.8 ms 122.4 ms
Jev 524.1 ms 536.0 ms

These are Cloudflare’s reported measurements; the available descriptions do not establish that an independent party reproduced them or that every model was tested under identical conditions. Treat them as a vendor comparison, not a latency guarantee for your deployment. Actual response time can depend on the model-hosting route and workload.

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Does Clef work with an existing Jev integration?

Cloudflare says Clef follows Jev’s System One API and that an existing integration can switch by changing the endpoint and model. In the launch changelog, Cloudflare puts it this way: “Clef follows the System One API, so you can switch an existing Jev integration to Clef by changing the endpoint and model.”

That is Cloudflare’s compatibility claim, not independent integration testing. Before switching production traffic, check the request and response schemas, supported question types, error handling, and the behavior of your actual workflow. Confirm that downstream systems handle the returned probabilities and allowed answers as expected.

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Open weights, hosted inference, and local testing

Cloudflare says it released Clef’s weights under the Apache 2.0 license and offers both variants through Workers AI. The Hugging Face model card describes a multimodal model that accepts text, JSON, images, or video, and documents local inference routes using Transformers, vLLM, SGLang, and Docker Model Runner.

The model card records testing with PyTorch 2.11 and Transformers 5.10.2 on a single H200. That is a documented test configuration, not a stated hardware requirement. Teams considering local inference should check the model card and the requirements of their chosen software stack against their own hardware.

Fine-tuning availability

Cloudflare announced hands-on fine-tuning support with a forward-deployed engineering team. It also described a self-serve fine-tuning platform as a future development, but the announcement does not specify a general-availability date for self-serve access.

How to assess Clef for your use case

  • Match the benchmark to the job: compare the task and metric that resemble your classification or routing problem; the listed benchmark results vary by model and dataset.
  • Measure your own workload: test response quality and latency with representative inputs, including cases that should be escalated or handled as uncertain.
  • Validate API behavior: treat System One compatibility as a starting point, then verify the integration and downstream handling in your environment.
  • Choose a deployment route: Workers AI is Cloudflare’s hosted option; the Apache 2.0 weights and documented inference tools offer paths for local experimentation.
  • Plan tuning accordingly: hands-on support has been announced, while self-serve availability remains unspecified in the launch materials.

Sources

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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