this-that 1.2 vs ScaleDown Classify-1
this-that 1.2 and ScaleDown Classify-1 have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | this-that 1.2 | ScaleDown Classify-1 |
|---|---|---|
| Provider | FLock.io | ScaleDown |
| Decision Index score | 24.6 | — |
| Calibration error (ECE) | 0.198 | — |
| Median latency (p50) | 44 ms | — |
| Cost per 1,000 decisions | — | $0.0080 |
| Parameters | 2B | — |
| Open weights | Yes | No |
| Question types | Choice (one of N options), Score (ordered rubric), Yes / no | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Decoder LM with a decision readout | Encoder classifier |
| License | mit | proprietary |
FAQ
Is this-that 1.2 or ScaleDown Classify-1 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, this-that 1.2 or ScaleDown Classify-1?
No calibration difference between this-that 1.2 and ScaleDown Classify-1 is recorded yet.
Is this-that 1.2 or ScaleDown Classify-1 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.