razorback16 openjev diffusiongemma (NVFP4, vLLM) vs amos-1-large
razorback16 openjev diffusiongemma (NVFP4, vLLM) and amos-1-large have no measured difference in accuracy, calibration or cost in our data yet.
| Spec | razorback16 openjev diffusiongemma (NVFP4, vLLM) | amos-1-large |
|---|---|---|
| Provider | razorback16 | Scira (mukaddamzaid) |
| Decision Index score | 40.0 | — |
| Calibration error (ECE) | 0.232 | — |
| Median latency (p50) | 38 ms | — |
| Cost per 1,000 decisions | — | — |
| Parameters | 26B | — |
| Open weights | Yes | Yes |
| Question types | Choice (one of N options), Score (ordered rubric), Yes / no | Choice (one of N options), Score (ordered rubric), Yes / no |
| Mechanism | Diffusion LM | Decoder LM with a decision readout |
| License | — | apache-2.0 |
FAQ
Is razorback16 openjev diffusiongemma (NVFP4, vLLM) or amos-1-large 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, razorback16 openjev diffusiongemma (NVFP4, vLLM) or amos-1-large?
No calibration difference between razorback16 openjev diffusiongemma (NVFP4, vLLM) and amos-1-large is recorded yet.
Is razorback16 openjev diffusiongemma (NVFP4, vLLM) or amos-1-large 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.