Clef vs ScaleDown

Clef

Apache-2.0 weights for all three models, ungated on Hugging Face. Clef-omni (9 October 2026) adds audio and video input. Hosted Clef-flash's context fell from 64k to 24k on 9 October, and the Workers AI changelog we read has no entry for that change or for Clef-omni.

ScaleDown

Classify answers Jev-shaped choice, noul and score questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.

Clef

1. Send model in the body as clef, clef-flash or clef-omni, matching the model in the URL. The input schema requires it
2. Ask every independent question about one state in one call. Up to 64 are allowed
3. Try clef-flash first for text-only triage under 24k tokens. It costs $0.038 per million input tokens against $0.24 for Clef
4. Send Clef-omni audio and video as base64 data URIs in audio and videos. Remote URLs are not accepted
5. Media and questions count towards the context window. If they exceed it, the request fails. Otherwise text state is truncated to fit
6. Read the internal code on a 429. 3036 means the day's 10,000 free neurons are spent, 3040 means capacity, so retry later
7. Treat an answer about user-supplied text, images, audio or video as a judgement that hostile input can steer. Cloudflare publishes no guidance on it

ScaleDown

1. Send several questions about one item in one /v1/scaledown call. Each becomes its own Classify call and the usage is summed
2. Pin classify-1, extract-1 or summarize-1 once you've tuned thresholds on your own labelled cases
3. Set context_chars to 0 and use top_n on Extract unless you need the 500 characters of context each side
4. Set reasoning: false on /classify when you only need the label. The reasoning call bills its input at 2x
5. Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects