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Model Comparison
Model Comparison
Compare performance, benchmarks, and characteristics
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Model Comparison
| Metric |  |  |
|---|---|---|
Pricing | ||
| Input Price | $1 / 1M tokens | $2 / 1M tokens |
| Output Price | $3 / 1M tokens | $8 / 1M tokens |
Capabilities | ||
| Context Window | 131072 tokens | 1047576 tokens |
| Capabilities | tools | tools |
| Input type | text | text, image |
Category Scores | ||
| Overall Average | 56.7 | 52.2 |
| Academia | 63.3 | 54.9 |
| Finance | 53.8 | 39.0 |
| Marketing | 57.6 | 59.0 |
| Maths | 57.3 | 34.7 |
| Programming | 38.1 | 32.2 |
| Science | 70.8 | 66.1 |
| Writing | 56.1 | 56.6 |
| Vision | N/A | 74.8 |
Benchmark Tests | ||
| AIME | _ | 43.7 |
| AA Coding Index | 38.1 | 32.2 |
| AAII | 50.4 | 43.4 |
| AA Math Index | 57.3 | 34.7 |
| GPQA | 76.3 | 66.5 |
| HLE | 6.3 | 4.6 |
| HumanEval | 94.5 | _ |
| LiveCodeBench | 61.0 | 45.7 |
| MATH-500 | _ | 91.3 |
| MMLU | 90.2 | 90.2 |
| MMLU-Pro | 82.2 | 80.6 |
| MMMU | _ | 74.8 |
| SciCode | 30.7 | 38.1 |
kimi-k2 Advantages
- • Higher benchmark scores (56.7 vs 52.2)
- • Lower input token cost ($1 / 1M tokens vs $2 / 1M tokens)
gpt-4.1 Advantages
- • Larger context window (1047576 tokens vs 131072 tokens)