Modelsamos-1-large
Amos 1 largeOur most capable model, for the hardest calls.
- Size27B
- Server time, median122 ms
- Context128K tokens
- Typed decisions, zero-shot76.1%
- Typed decisions, fitted80.3%
- Calibration, KL0.126
- Decision Bench94.9%
- Decision Index 0.3, public65.11
When to use it
- Questions that take several steps.
- Long policies and trade-offs.
- Questions that need broad knowledge.
- The best Decision Index, photo and calibration scores of the four.
- Cases up to 128K tokens.
Typed decisions
Accuracy on the 2,000-decision test split, zero-shot. We ran each rival on the same 2,000 decisions; Decider 1's score is its makers' own. Frontier language models asked the same questions score between 73 and 78.
- Microsoft-Decision-1closed weights, KL 0.31879.5
- Decider 176.8
- Amos 1 medium76.5
- Amos 1 large76.1
- Perplexity Decider v1.173.9
- Clef72.5
- GPT-6 Luna71.9
- Amos 1 small69.5
- Amos 1 mini64.6
Quotes and photos
- Invoice totals quoted exactly10 / 10
- Box centred on the named object, RefCOCO91.9%
- Boxes covering at least half the object, RefCOCO71.3%
- Box centred on the named object, COCO98.4%
Limits
- The slowest of the four.
- Long quotes, such as a whole clause or address, can come back cut short.
- English only.
Quick start
Name the model in the model field.
curl https://api.amos.scira.ai/v1/systemone \
-H 'Content-Type: application/json' \
-d '{
"model": "amos-1-large",
"state": "Hi, invoice INV-2231 for £306.00 was charged to my card twice this morning. Please refund one of them today.",
"questions": {
"refund": {
"type": "noul",
"instructions": "Is the customer asking for a refund?"
},
"team": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": null,
"technical": null,
"sales": null
}
},
"amount": {
"type": "span",
"instructions": "How much was charged?"
}
}
}'