Celeris-1 Decision vs Kev

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.

Kev

Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models. No package. pip install kev installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv.

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.

Kev

1. Install from the repository. The kev package on PyPI is an unrelated project
2. Pin a checkpoint with @v1.0, as in jaredpalmer/kev-4b@v1.0, so tuned thresholds keep their meaning
3. Keep states under 8,192 tokens on Kev-0.8B, 4B and 9B, or use Kev-27B for long documents
4. Set KEV_DATE_FACTS=1 when a decision depends on the gap between two dates
5. Expect a 422 naming the token count when a state passes 65,536 tokens. The server refuses it instead of cutting it