Celeris-1 Decision vs kettei

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.

kettei

OneHot returns probabilities for up to 128 typed questions a call at $0.02 to $0.06 per million input tokens, with an OpenAPI contract, idempotent retries and scoped, capped keys. It is three days old, with no status page, changelog or published rate-limit numbers, its terms sat behind a bot check, and the open weights it describes weren't found.

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.

kettei

1. Send context and decisions to POST https://api.sqwish.ai/v1/decide with a bearer key. Leaving out model gets kettei-core, which reads text only
2. Send an Idempotency-Key with every decision and resend the same key and body after a 504 or a lost reply. Replays aren't charged
3. Branch on error.code and error.retryable, not the status. Wait for Retry-After on 429 and retryable 503s
4. Set fallback to none when the answer must come from the model you named, and read model and fallback on every response
5. Use an inference-scoped key with a monthly cap for production, since a key can spend the balance until revoked