amos-1-large vs GLiNER2.5-Decide
amos-1-large and GLiNER2.5-Decide have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-large | GLiNER2.5-Decide |
|---|---|---|
| Provider | Scira (mukaddamzaid) | Fastino |
| Decision Index score | — | 9.2 |
| Calibration error (ECE) | — | 0.088 |
| Median latency (p50) | — | 23 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | — | 486M |
| 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, Extraction (typed spans) |
| Mechanism | Decoder LM with a decision readout | Span encoder (GLiNER family) |
| License | apache-2.0 | apache-2.0 |
FAQ
Is amos-1-large or GLiNER2.5-Decide 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 GLiNER2.5-Decide?
No calibration difference between amos-1-large and GLiNER2.5-Decide is recorded yet.
Is amos-1-large or GLiNER2.5-Decide 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.