Liquid d1 vs kettei

Liquid d1

The hosted API costs $0.02 per million input tokens for d1 and $0.042 for d1-ultra, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found.

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.

Liquid d1

1. POST to https://api.liquid.ai/decisions/v1/systemone with a liquid_ key as a Bearer header. This is not a chat-completions endpoint
2. Use d1, or d1-ultra for harder decisions at $0.042 against $0.02 per million input tokens. d1:free is no longer documented
3. Send images as Base64 data URLs in images, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused
4. Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again
5. Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data

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