GLiNER2.5-Decide vs metask-jev-rain-31B-trained
GLiNER2.5-Decide and metask-jev-rain-31B-trained have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | GLiNER2.5-Decide | metask-jev-rain-31B-trained |
|---|---|---|
| Provider | Fastino | MeTask-Rain |
| Decision Index score | 10.1 | — |
| Calibration error (ECE) | 0.088 | — |
| Median latency (p50) | 23 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 486M | 33B |
| 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 | apache-2.0 |
FAQ
Is GLiNER2.5-Decide or metask-jev-rain-31B-trained 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, GLiNER2.5-Decide or metask-jev-rain-31B-trained?
No calibration difference between GLiNER2.5-Decide and metask-jev-rain-31B-trained is recorded yet.
Is GLiNER2.5-Decide or metask-jev-rain-31B-trained 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.