amos-1-large vs Sieve 9B
amos-1-large and Sieve 9B have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-large | Sieve 9B |
|---|---|---|
| Provider | Scira (mukaddamzaid) | Sthānika AI |
| Decision Index score | — | 44.6 |
| Calibration error (ECE) | — | 0.043 |
| Median latency (p50) | — | 50 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | — | 10B |
| Open weights | Yes | Yes |
| Question types | Choice (one of N options), Score (ordered rubric), Yes / no | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Decoder LM with a decision readout | Decoder LM with a decision readout |
| License | apache-2.0 | apache-2.0 |
FAQ
Is amos-1-large or Sieve 9B more accurate?
At least one of the two has no Jev Decision Index score yet, so their accuracy cannot be compared here.
Which is better calibrated, amos-1-large or Sieve 9B?
No calibration difference between amos-1-large and Sieve 9B is recorded yet.
Is amos-1-large or Sieve 9B cheaper per 1,000 decisions?
A hosted price is not recorded for both models; open-weight models cost the compute you run them on.
Cost per 1,000 decisions assumes 200 input tokens per decision. Pricing is indicative — confirm with the provider before production use. Highlighted values indicate the better figure for that row.