amos-1-large vs jeff
amos-1-large and jeff have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-large | jeff |
|---|---|---|
| Provider | Scira (mukaddamzaid) | logan-markewich |
| Decision Index score | — | 7.7 |
| Calibration error (ECE) | — | 0.097 |
| Median latency (p50) | — | 22 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | — | 576M |
| 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 | Span encoder (GLiNER family) |
| License | apache-2.0 | — |
FAQ
Is amos-1-large or jeff 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-large or jeff?
No calibration difference between amos-1-large and jeff is recorded yet.
Is amos-1-large or jeff 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.