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Model Comparison
Model Comparison
Compare performance, benchmarks, and characteristics
mixtral-8x7b-instruct-v0.1
bedrock
Context32K tokens
Input Price$0.45 / 1M tokens
Output Price$0.7 / 1M tokens
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Model Comparison
| Metric |  |  |
|---|---|---|
Pricing | ||
| Input Price | $0.45 / 1M tokens | $2 / 1M tokens |
| Output Price | $0.7 / 1M tokens | $8 / 1M tokens |
Capabilities | ||
| Context Window | 32K tokens | 1047576 tokens |
| Capabilities | tools | |
| Input type | text | text, image |
Category Scores | ||
| Overall Average | 12.3 | 56.8 |
| Academia | 15.9 | 54.9 |
| Finance | 8.8 | 55.5 |
| Marketing | 3.2 | 54.4 |
| Maths | 15.0 | 67.5 |
| Programming | 4.7 | 41.9 |
| Science | 29.2 | 66.5 |
| Writing | 9.2 | 39.2 |
| Vision | N/A | 74.8 |
Benchmark Tests | ||
| AIME | 0.0 | 43.7 |
| AA Coding Index | 4.7 | 41.9 |
| AAII | 2.6 | 43.4 |
| AA Math Index | 15.0 | 67.5 |
| GPQA | 29.2 | 66.5 |
| HLE | 4.5 | 4.6 |
| LiveCodeBench | 6.6 | 45.7 |
| MATH-500 | 29.9 | 91.3 |
| MMLU | _ | 90.2 |
| MMLU-Pro | 38.7 | 80.6 |
| MMMU | _ | 74.8 |
| SciCode | 2.8 | 38.1 |
mixtral-8x7b-instruct-v0.1 Advantages
- • Lower input token cost ($0.45 / 1M tokens vs $2 / 1M tokens)
gpt-4.1 Advantages
- • Higher benchmark scores (56.8 vs 12.3)
- • Larger context window (1047576 tokens vs 32K tokens)