ScaleDown vs Jev

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.

Jev

Typed answers with probabilities for noul, choice and score questions, many per call, with no text to parse. Early access behind a waitlist, with no free tier or free credits found.

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

Jev

1. Put every independent question about one state into a single call. They run in parallel and the state is billed once
2. Pin jev-1.13.0 instead of jev-latest once you've tuned confidence thresholds
3. Back off exponentially on 429 and 529. The limits move with demand
4. Keep state to what the decision needs. Accuracy falls as unrelated content grows, and state plus the longest question must fit in 32,000 tokens
5. Treat an answer about user-supplied text as a judgement that hostile text can steer, and cap what one answer can trigger