Celeris-1 Decision vs ScaleDown

Celeris-1 Decision

Typed probabilities, image inputs and optional explanations suit routing and classification inside an agent. Both System One and OpenAI Decisions request formats are documented. The service remains early access under its terms, activation can queue, and prepaid credit expires after 30 days. The published accuracy and latency figures are Celeris measurements, not Anchor Terminal tests.

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.

Celeris-1 Decision

1. Use the decision model path and matching model field. This model has no chat, Responses or models endpoint.
2. Use at most 512 questions per request, or 64 with explanations. Both endpoints reject streaming.
3. Preserve x_celeris in a custom Jev SDK response model when reading explanations. The default response type drops it.
4. Retry 429 with Retry-After and backoff. Stop on 402 until workspace credit is replenished.
5. Read usage.input_tokens for cost, including images and request overhead. Track credit expiry separately from token consumption.

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