GLiNER2.5-Decide vs Winnow-E4B
Winnow-E4B is more accurate on the Jev Decision Index, Winnow-E4B is better calibrated. Choose on whichever of accuracy, calibration and cost matters most for your decisions.
| Spec | GLiNER2.5-Decide | Winnow-E4B |
|---|---|---|
| Provider | Fastino | EldanRing |
| Decision Index score | 10.1 | 32.7 |
| Calibration error (ECE) | 0.088 | 0.058 |
| Median latency (p50) | 23 ms | 45 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | 486M | 8B |
| 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 Winnow-E4B more accurate?
Winnow-E4B scores higher on the Jev Decision Index (32.7 vs 10.1).
Which is better calibrated, GLiNER2.5-Decide or Winnow-E4B?
Winnow-E4B has the lower expected calibration error (0.058 vs 0.088), so its stated confidence is closer to how often it is right.
Is GLiNER2.5-Decide or Winnow-E4B 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.