Hopper 12B (trained) vs amos-1-large
Hopper 12B (trained) and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | Hopper 12B (trained) | amos-1-large |
|---|---|---|
| Provider | HopitAI | Scira (mukaddamzaid) |
| Decision Index score | 47.3 | — |
| Calibration error (ECE) | 0.076 | — |
| Median latency (p50) | 290 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 12B | — |
| 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 | research-and-demo | apache-2.0 |
FAQ
Is Hopper 12B (trained) or amos-1-large 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, Hopper 12B (trained) or amos-1-large?
No calibration difference between Hopper 12B (trained) and amos-1-large is recorded yet.
Is Hopper 12B (trained) or amos-1-large 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.