GLiNER2.5-Decide vs JPT-0.8B
JPT-0.8B is more accurate on the Jev Decision Index, JPT-0.8B is better calibrated. Choose on whichever of accuracy, calibration and cost matters most for your decisions.
| Spec | GLiNER2.5-Decide | JPT-0.8B |
|---|---|---|
| Provider | Fastino | kirp |
| Decision Index score | 10.1 | 15.5 |
| Calibration error (ECE) | 0.088 | 0.069 |
| Median latency (p50) | 23 ms | 118 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | 486M | 873M |
| Open weights | Yes | Yes |
| Question types | Choice (one of N options), Score (ordered rubric), Yes / no, Extraction (typed spans) | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Span encoder (GLiNER family) | Decoder LM with a decision readout |
| License | apache-2.0 | cc-by-nc-4.0 |
FAQ
Is GLiNER2.5-Decide or JPT-0.8B more accurate?
JPT-0.8B scores higher on the Jev Decision Index (15.5 vs 10.1).
Which is better calibrated, GLiNER2.5-Decide or JPT-0.8B?
JPT-0.8B has the lower expected calibration error (0.069 vs 0.088), so its stated confidence is closer to how often it is right.
Is GLiNER2.5-Decide or JPT-0.8B 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.