GLiNER2.5-Decide vs Sieve 27B
Sieve 27B is more accurate on the Jev Decision Index, Sieve 27B is better calibrated. Choose on whichever of accuracy, calibration and cost matters most for your decisions.
| Spec | GLiNER2.5-Decide | Sieve 27B |
|---|---|---|
| Provider | Fastino | Sthānika AI |
| Decision Index score | 10.1 | 60.2 |
| Calibration error (ECE) | 0.088 | 0.065 |
| Median latency (p50) | 23 ms | 101 ms |
| Cost per 1,000 decisions | — | — |
| Parameters | 486M | 28B |
| 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 Sieve 27B more accurate?
Sieve 27B scores higher on the Jev Decision Index (60.2 vs 10.1).
Which is better calibrated, GLiNER2.5-Decide or Sieve 27B?
Sieve 27B has the lower expected calibration error (0.065 vs 0.088), so its stated confidence is closer to how often it is right.
Is GLiNER2.5-Decide or Sieve 27B 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.