LiquidAI d1-3B vs amos-1-medium
LiquidAI d1-3B and amos-1-medium have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | LiquidAI d1-3B | amos-1-medium |
|---|---|---|
| Provider | Liquid AI | Scira (mukaddamzaid) |
| Decision Index score | 38.9 | — |
| Calibration error (ECE) | 0.047 | — |
| Median latency (p50) | 16 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 3B | — |
| 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 | lfm1.0 | apache-2.0 |
FAQ
Is LiquidAI d1-3B 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, LiquidAI d1-3B or amos-1-medium?
No calibration difference between LiquidAI d1-3B and amos-1-medium is recorded yet.
Is LiquidAI d1-3B 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.