LiquidAI d1-omni-600M vs amos-1-large
LiquidAI d1-omni-600M and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | LiquidAI d1-omni-600M | amos-1-large |
|---|---|---|
| Provider | Liquid AI | Scira (mukaddamzaid) |
| Decision Index score | 9.1 | — |
| Calibration error (ECE) | 0.128 | — |
| Median latency (p50) | 11 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 587M | — |
| 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 | Encoder classifier | Decoder LM with a decision readout |
| License | lfm1.0 | apache-2.0 |
FAQ
Is LiquidAI d1-omni-600M or amos-1-large 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-omni-600M or amos-1-large?
No calibration difference between LiquidAI d1-omni-600M and amos-1-large is recorded yet.
Is LiquidAI d1-omni-600M or amos-1-large 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.