ScaleDown vs Strands Decider 2B

ScaleDown

Classify answers Jev-shaped choice, noul and score questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.

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.

ScaleDown

1. Send several questions about one item in one /v1/scaledown call. Each becomes its own Classify call and the usage is summed
2. Pin classify-1, extract-1 or summarize-1 once you've tuned thresholds on your own labelled cases
3. Set context_chars to 0 and use top_n on Extract unless you need the 500 characters of context each side
4. Set reasoning: false on /classify when you only need the label. The reasoning call bills its input at 2x
5. Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects

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