GLiClass large v3.0 vs amos-1-medium
GLiClass large v3.0 and amos-1-medium have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | GLiClass large v3.0 | amos-1-medium |
|---|---|---|
| Provider | Knowledgator | Scira (mukaddamzaid) |
| Decision Index score | — | — |
| Calibration error (ECE) | — | — |
| Median latency (p50) | — | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 439M | — |
| Open weights | Yes | Yes |
| Question types | Choice (one of N options), Yes / no | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Encoder classifier | Decoder LM with a decision readout |
| License | Apache 2.0 | apache-2.0 |
FAQ
Is GLiClass large v3.0 or amos-1-medium 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, GLiClass large v3.0 or amos-1-medium?
No calibration difference between GLiClass large v3.0 and amos-1-medium is recorded yet.
Is GLiClass large v3.0 or amos-1-medium 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.