amos-1-medium vs RSI-Jev v6.1 VL 27B (effort auto)
amos-1-medium and RSI-Jev v6.1 VL 27B (effort auto) have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | amos-1-medium | RSI-Jev v6.1 VL 27B (effort auto) |
|---|---|---|
| Provider | Scira (mukaddamzaid) | shgao |
| Decision Index score | — | 58.7 |
| Calibration error (ECE) | — | 0.077 |
| Median latency (p50) | — | 76 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | — | 28B |
| 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-medium or RSI-Jev v6.1 VL 27B (effort auto) 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-medium or RSI-Jev v6.1 VL 27B (effort auto)?
No calibration difference between amos-1-medium and RSI-Jev v6.1 VL 27B (effort auto) is recorded yet.
Is amos-1-medium or RSI-Jev v6.1 VL 27B (effort auto) 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.