Head to head
Classify-1 scores 30.6 and Jev 1.14 79.3 on the System One Index. Classify-1 is more accurate on 1 of 16 text datasets, Jev 1.14 on 15. Same cases, same request, same scorer.
| Classify-1 | Jev 1.14 | |
|---|---|---|
| System One Index | 30.6 | 79.3 |
| English | 30.7 | 78.8 |
| Arabic | 30.6 | 80.3 |
| Vision Index | – | 85.1 |
| Calibration error | 3.2% | 6.0% |
| Median latency | 254 ms | 136 ms |
| Cost per 1,000 calls | $0.018 | $0.028 |
| Classify-1 | Jev 1.14 | |
|---|---|---|
| AG News English | 26.5 | 89.0 |
| SST-2 English | 50.0 | 95.0 |
| SST-5 English | 13.0 | 60.0 |
| DAIR Emotion English | 59.0 | 57.5 |
| Banking77 English | 1.0 | 78.5 |
| BoolQ English | 38.0 | 90.0 |
| XNLI English English | 30.7 | 86.0 |
| Typed decisions English | 27.2 | 74.2 |
| XNLI French French | 30.7 | 76.7 |
| XNLI Arabic Arabic | 30.7 | 71.3 |
| MASSIVE scenario Arabic | 3.0 | 65.0 |
| MASSIVE intent Arabic | 3.5 | 85.0 |
| OSACT4 offensive Arabic | 80.0 | 87.0 |
| Tweet sentiment Arabic | 35.0 | 76.0 |
| MIRACL rerank Arabic | 12.0 | 87.5 |
| MIRACL pairwise Arabic | 50.0 | 90.0 |
| Classify-1 | Jev 1.14 | |
|---|---|---|
| SST-2, as an image | – | 93.5 |
| Arabic sentiment, as an image | – | 63.5 |
| Oxford-IIIT Pet | – | 88.5 |
| VQAv2 yes/no | – | 86.5 |
| CountBench | – | 93.5 |
Model pages: Classify-1 · Jev 1.14. Latency for API models is the median per call through OpenRouter, timed from a cloud server with 8 calls in flight.