Nirnaya 0.4B vs amos-1-large
Nirnaya 0.4B and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | Nirnaya 0.4B | amos-1-large |
|---|---|---|
| Provider | kshitijthakkar | Scira (mukaddamzaid) |
| Decision Index score | 0.0 | — |
| Calibration error (ECE) | 0.046 | — |
| Median latency (p50) | 14 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 390M | — |
| 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 |
FAQ
Is Nirnaya 0.4B 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, Nirnaya 0.4B or amos-1-large?
No calibration difference between Nirnaya 0.4B and amos-1-large is recorded yet.
Is Nirnaya 0.4B 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.