Laya vs ScaleDown

Laya

Apache-2.0 code and weights, installed with pip install laya, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.

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.

Laya

1. Set LAYA_API_KEY before starting laya-serve. It listens on every interface by default
2. Gate on answer_confidence, not confidence, which measures entropy and doesn't match Jev's field
3. Shortlist choice questions with more than about 20 options using predict_shortlist or the laya_shortlist tool
4. Use semantic or opaque labels such as A and B, not yes and no, in choice questions. The checkpoints can follow the label text
5. Pass model="multilingual" and max_len=8192 for long documents. The English checkpoint stops at 512 tokens

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