I'm a big fan of Cloudflare products. I recently stuck Jev in front of a Cloudflare-hosted Ollama model for chat/username moderation, so I was excited to test out Clef. The setup is user send a message -> Jev does first pass to see if it's toxic/hate speech/profane, if Jev is unsure then Ollama on Workers AI takes a deeper look.

Clef was 2-3x slower and worse (it caught less hate speech) than Jev. Overall disappointing.

In "How we trained Clef" section Cloudflare mentioned that they initially based their Jev-like system on DiffussionGemma (DJev) model but then changed to Qwen with post training for Clef, but never mentioned any reason and justification for the change.

Although they included the benchmark against DJev and Clef is better, perhaps if you can test it to see the real-world performance.

Diffusion Gemma sucks to train if you are not already mostly in distribution. The reason diffusion Gemma is fast is a learned denoising that balances quality and speed. So the further your data is from what already happens, the slower it gets. In almost every case I’ve tried, you might as well just train Nemotron.

It's likely no coindicence that they only show "median latency", not how latency scales with input size.

For small inputs Jev is slow, but its latency curve is very flat. Fine-tuning a decently-sized llm (like this 27B model) gives you something that's faster on small input sizes, but even with moderate contexts quickly becomes much slower than Jev. Characterizing it as "faster than Jev" is very misleading, unless you know all your questions have tiny context (less than 1k tokens or so)

True. But do I understand that Clef is multimodal and Jev is only text?

yes. Jev is text-only, while Clef and a lot of the other alternatives are fine-tunes of multi-modal models, so you get image-input basically for free. Actual decision quality based on images is a bit up in the air though, I am not aware of any benchmarks testing that

Fable orchestrating DeepSeek v4 Flash to implement a plan is my new favorite thing.

It's so freaking fast, but you gotta tell Fable to watch Deepseek like a hawk or it'll go off the rails.

Yes. It works very well for simple tasks. When I know the context grows over 200k, I implement with Kimi.

We run an agent company and we do a bunch of different things with agents. Where we used Gemini before Deepseek v4 Flash is taking the lead on price. It's like 5x cheaper than 3.6 and well 2.5x cheaper than 3.7 "introductory price". Comparable quality.

Interesting - how are you interacting and orchestrating this?

Not the parent, but:

https://omp.sh/

You define roles for different agents like this:

  modelRoles: 
    task: fireworks/kimi-k3-fast:high
    plan: fireworks/kimi-k3-fast:max
    slow: fireworks/kimi-k3-fast:max
    smol: fireworks/deepseek-v4-flash-0731:low
    tiny: fireworks/gpt-oss-20b
    vision: fireworks/qwen3.7-plus:high
    designer: fireworks/qwen3.7-plus:high
    advisor: openai-codex/gpt-5.6-sol:high
    main_worker: fireworks/kimi-k3-fast:high
    fast_worker: fireworks/deepseek-v4-flash-0731:low
    vision_worker: fireworks/qwen3.7-plus:high
    research_worker: fireworks/glm-5.2:medium
    code_worker: fireworks/kimi-k2.7-code-fast:high
    review_worker: anthropic/claude-fable-5:high
    security_review_worker: fireworks/kimi-k3-fast:max
    minimal_worker: fireworks/gpt-oss-20b
    default: fireworks/kimi-k3-fast
  task: 
    agentModelOverrides: 
      task: "@main_worker"
      sonic: "@fast_worker"
      scout: "@fast_worker"
      designer: "@vision_worker"
      librarian: "@research_worker"
      reviewer: "@review_worker"
      security-reviewer: "@security_review_worker"

Then you first say /plan and use some big model like K3. Finally the harness shows you a markdown you approve, and in approval you switch to a smaller model and reset the context. The smaller model gets the full plan and starts working on it. When done, you say /review and it spawns N review agents and returns the change suggestions. And you iterate on that.

I loved Superpowers and evangelized it heavily.

This week I removed it because it now gets in the way of the frontier models.

I don't know if its still the case anymore since 1.3 but Ghostty did have a documented issue with Claude causing resource issues.