Open Jev JSON Canvas vs amos-1-large
Open Jev JSON Canvas and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | Open Jev JSON Canvas | amos-1-large |
|---|---|---|
| Provider | JoshuaSP | Scira (mukaddamzaid) |
| Decision Index score | — | — |
| Calibration error (ECE) | — | — |
| Median latency (p50) | — | — |
| Cost per 1,000 decisions | — | — |
| Parameters | — | — |
| 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 |
FAQ
Is Open Jev JSON Canvas 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, Open Jev JSON Canvas or amos-1-large?
No calibration difference between Open Jev JSON Canvas and amos-1-large is recorded yet.
Is Open Jev JSON Canvas 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.