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A head-to-head benchmark comparison of Pangu Pro MoE across 17 evaluations.
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning .
Full Pangu Pro MoE specs →| Benchmark | Pangu Pro MoE |
|---|---|
| code | |
| LiveCodeBench | 59.6% |
| MBPP+ | 80.2% |
| general | |
| ArenaHard | 93.6% |
| C-Eval | 91.1% |
| CLUEWSC | 94.7% |
| IFEval | 85.7% |
| MMLU | 89.3% |
| MMLU-Pro | 82.6% |
| MMLU-Redux | 81.5% |
| knowledge | |
| CMMLU | 87.1 |
| SuperGPQA | 54.8 |
| math | |
| AIME 2024 | 79.2% |
| AIME 2025 | 68.1% |
| CNMO 2024 | 70.8% |
| MATH-500 | 96.8% |
| reasoning | |
| DROP | 91.2% |
| GPQA Diamond | 73.7% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Pangu Pro MoE has the strongest result on LiveCodeBench among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 17 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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