GLiNER2.5-Decide vs Qwen Decision 27B noncausal LoRA
Qwen Decision 27B noncausal LoRA is more accurate on the Jev Decision Index, Qwen Decision 27B noncausal LoRA is better calibrated. Choose on whichever of accuracy, calibration and cost matters most for your decisions.
| Spec | GLiNER2.5-Decide | Qwen Decision 27B noncausal LoRA |
|---|---|---|
| Provider | Fastino | jsaurabh |
| Decision Index score | 10.1 | 61.1 |
| Calibration error (ECE) | 0.088 | 0.051 |
| Median latency (p50) | 23 ms | 127 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 Qwen Decision 27B noncausal LoRA more accurate?
Qwen Decision 27B noncausal LoRA scores higher on the Jev Decision Index (61.1 vs 10.1).
Which is better calibrated, GLiNER2.5-Decide or Qwen Decision 27B noncausal LoRA?
Qwen Decision 27B noncausal LoRA has the lower expected calibration error (0.051 vs 0.088), so its stated confidence is closer to how often it is right.
Is GLiNER2.5-Decide or Qwen Decision 27B noncausal LoRA 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.