nano-jev vs amos-1-large
nano-jev and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | nano-jev | amos-1-large |
|---|---|---|
| Provider | sdmlai | Scira (mukaddamzaid) |
| Decision Index score | — | — |
| Calibration error (ECE) | — | — |
| Median latency (p50) | — | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 33M | — |
| 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 | Cross-encoder (NLI) | Decoder LM with a decision readout |
| License | mit | apache-2.0 |
FAQ
Is nano-jev 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, nano-jev or amos-1-large?
No calibration difference between nano-jev and amos-1-large is recorded yet.
Is nano-jev 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.