Celeris-1 Decision vs Strands Decider 2B

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.

Strands Decider 2B

A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.

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.

Strands Decider 2B

1. Pin the checkpoint by its full name, such as StrandsAgents/strands-decider-2B-hobson-v21, since each version is a separate Hugging Face repository
2. Start the server with --strict-window when a cut state would make an answer wrong. It then returns 422 naming the window
3. Ask every question about one state in one request. The state is read once and each question adds only its own tokens
4. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option
5. Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only