amos-1-large vs LiquidAI d1-3B
amos-1-large and LiquidAI d1-3B have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-large | LiquidAI d1-3B |
|---|---|---|
| Provider | Scira (mukaddamzaid) | Liquid AI |
| 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 | apache-2.0 | lfm1.0 |
FAQ
Is amos-1-large or LiquidAI d1-3B 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 LiquidAI d1-3B?
No calibration difference between amos-1-large and LiquidAI d1-3B is recorded yet.
Is amos-1-large or LiquidAI d1-3B 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.