Decision 3.0 vs kettei

Decision 3.0

Open Apache-2.0 decision models that read images and video as well as text and answer several typed questions in one call, with a probability per option and tagged Hub releases. They are two days old, need a GPU and trust_remote_code, drop Vela's set and span types, and publish no calibration figures.

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.

Decision 3.0

1. Pin a tag or commit with revision= when loading from the Hub. Each model had five tagged releases on 10 and 11 October
2. Send images and videos to d3_server.py as base64 data URLs only. The server refuses paths and http URLs with 422
3. Set DECISION_API_KEY before binding the bundled server beyond 127.0.0.1. It is open otherwise
4. Turn on permutation_average=True when routing must not depend on option order. It is off by default
5. Expect one request at a time from the bundled server. Use the router's model runtime for queued serving

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