amos-1-medium vs Sieve 27B
amos-1-medium and Sieve 27B have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-medium | Sieve 27B |
|---|---|---|
| Provider | Scira (mukaddamzaid) | Sthānika AI |
| 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 amos-1-medium or Sieve 27B 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-medium or Sieve 27B?
No calibration difference between amos-1-medium and Sieve 27B is recorded yet.
Is amos-1-medium or Sieve 27B 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.