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Model Comparison
Model Comparison
Compare performance, benchmarks, and characteristics
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Model Comparison
| Metric |  |  |
|---|---|---|
Pricing | ||
| Input Price | _ | $3 / 1M tokens |
| Output Price | _ | $15 / 1M tokens |
Capabilities | ||
| Context Window | 128K tokens | 256K tokens |
| Capabilities | tools | tools |
| Input type | text | text |
Category Scores | ||
| Overall Average | 38.7 | 71.3 |
| Academia | 46.9 | 76.4 |
| Marketing | 26.7 | 51.5 |
| Programming | 45.5 | 63.8 |
| Science | 55.7 | 87.6 |
| Writing | 18.9 | 42.2 |
| Finance | N/A | 81.0 |
| Maths | N/A | 96.7 |
Benchmark Tests | ||
| AIME | _ | 94.3 |
| AA Coding Index | 45.5 | 63.8 |
| AAII | 38.1 | 65.3 |
| AA Math Index | _ | 96.7 |
| GPQA | 55.7 | 87.6 |
| HLE | 4.0 | 23.9 |
| LiveCodeBench | 70.1 | 81.9 |
| MATH-500 | _ | 99.0 |
| MMLU-Pro | 73.9 | 86.6 |
| SciCode | 20.9 | 45.7 |
nemotron-nano-9b-v2 Advantages
- • Lower input token cost (_ vs $3 / 1M tokens)
grok-4 Advantages
- • Higher benchmark scores (71.3 vs 38.7)
- • Larger context window (256K tokens vs 128K tokens)