amos-1-large vs vLLM-SR Decision 2.0 Nox 4B
amos-1-large and vLLM-SR Decision 2.0 Nox 4B have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-large | vLLM-SR Decision 2.0 Nox 4B |
|---|---|---|
| Provider | Scira (mukaddamzaid) | vLLM Semantic Router |
| Decision Index score | — | 45.0 |
| Calibration error (ECE) | — | 0.030 |
| Median latency (p50) | — | 34 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | — | 4B |
| 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 | apache-2.0 |
FAQ
Is amos-1-large or vLLM-SR Decision 2.0 Nox 4B 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 vLLM-SR Decision 2.0 Nox 4B?
No calibration difference between amos-1-large and vLLM-SR Decision 2.0 Nox 4B is recorded yet.
Is amos-1-large or vLLM-SR Decision 2.0 Nox 4B 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.