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Model Comparison
Model Comparison
Compare performance, benchmarks, and characteristics
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Model Comparison
| Metric |  |  |
|---|---|---|
Pricing | ||
| Input Price | $0.6 / 1M tokens | $2 / 1M tokens |
| Output Price | $1.8 / 1M tokens | $8 / 1M tokens |
Capabilities | ||
| Context Window | 65536 tokens | 1047576 tokens |
| Capabilities | tools | tools |
| Input type | text, image | text, image |
Category Scores | ||
| Overall Average | 32.9 | 53.4 |
| Academia | 41.6 | 54.9 |
| Finance | 20.6 | 39.0 |
| Marketing | 32.8 | 59.0 |
| Maths | 15.3 | 34.7 |
| Programming | 27.0 | 41.9 |
| Science | 58.3 | 66.1 |
| Writing | 34.9 | 56.6 |
| Vision | N/A | 74.8 |
Benchmark Tests | ||
| AIME | _ | 43.7 |
| AA Coding Index | 27.0 | 41.9 |
| AAII | 26.0 | 43.4 |
| AA Math Index | 15.3 | 34.7 |
| GPQA | 57.3 | 66.5 |
| HLE | 3.6 | 4.6 |
| LiveCodeBench | 35.2 | 45.7 |
| MATH-500 | _ | 91.3 |
| MMLU | _ | 90.2 |
| MMLU-Pro | 75.1 | 80.6 |
| MMMU | _ | 74.8 |
| SciCode | 18.8 | 38.1 |
glm-4.5v Advantages
- • Lower input token cost ($0.6 / 1M tokens vs $2 / 1M tokens)
gpt-4.1 Advantages
- • Higher benchmark scores (53.4 vs 32.9)
- • Larger context window (1047576 tokens vs 65536 tokens)