Sieve 27B vs amos-1-medium
Sieve 27B and amos-1-medium have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | Sieve 27B | amos-1-medium |
|---|---|---|
| Provider | Sthānika AI | Scira (mukaddamzaid) |
| Decision Index score | 60.2 | — |
| Calibration error (ECE) | 0.065 | — |
| Median latency (p50) | 101 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 28B | — |
| 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 Sieve 27B or amos-1-medium 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, Sieve 27B or amos-1-medium?
No calibration difference between Sieve 27B and amos-1-medium is recorded yet.
Is Sieve 27B or amos-1-medium 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.