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Model Comparison
Model Comparison
Compare performance, benchmarks, and characteristics
kimi-k2
deepinfra
Context131K tokens
Input Price$0.5 / 1M tokens
Output Price$2 / 1M tokens
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Model Comparison
| Metric |  |  |
|---|---|---|
Pricing | ||
| Input Price | $0.5 / 1M tokens | $2 / 1M tokens |
| Output Price | $2 / 1M tokens | $8 / 1M tokens |
Capabilities | ||
| Context Window | 131K tokens | 1047576 tokens |
| Capabilities | tools | tools |
| Input type | text | text, image |
Category Scores | ||
| Overall Average | 60.3 | 56.8 |
| Academia | 62.3 | 54.9 |
| Finance | 65.7 | 55.5 |
| Marketing | 55.8 | 54.4 |
| Maths | 83.2 | 67.5 |
| Programming | 36.5 | 41.9 |
| Science | 76.6 | 66.5 |
| Writing | 41.8 | 39.2 |
| Vision | N/A | 74.8 |
Benchmark Tests | ||
| AIME | 69.3 | 43.7 |
| AA Coding Index | 36.5 | 41.9 |
| AAII | 48.1 | 43.4 |
| AA Math Index | 83.2 | 67.5 |
| GPQA | 76.6 | 66.5 |
| HLE | 7.0 | 4.6 |
| LiveCodeBench | 55.6 | 45.7 |
| MATH-500 | 97.1 | 91.3 |
| MMLU | 87.8 | 90.2 |
| MMLU-Pro | 82.4 | 80.6 |
| MMMU | _ | 74.8 |
| SciCode | 34.5 | 38.1 |
kimi-k2 Advantages
- • Higher benchmark scores (60.3 vs 56.8)
- • Lower input token cost ($0.5 / 1M tokens vs $2 / 1M tokens)
gpt-4.1 Advantages
- • Larger context window (1047576 tokens vs 131K tokens)