Celeris-1 Decision vs Dinah-0
Celeris-1 Decision and Dinah-0 have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | Celeris-1 Decision | Dinah-0 |
|---|---|---|
| Provider | Celeris | Lukitaduarte |
| Decision Index score | — | 13.9 |
| Calibration error (ECE) | — | 0.104 |
| Median latency (p50) | — | 14 ms |
| Cost per 1,000 decisions | $0.0080 | — |
| Parameters | — | 150M |
| Open weights | No | Yes |
| Question types | Choice (one of N options), Score (ordered rubric), Yes / no | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Diffusion LM | Encoder classifier |
| License | proprietary | cc-by-nc-4.0 |
FAQ
Is Celeris-1 Decision or Dinah-0 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, Celeris-1 Decision or Dinah-0?
No calibration difference between Celeris-1 Decision and Dinah-0 is recorded yet.
Is Celeris-1 Decision or Dinah-0 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.