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A head-to-head benchmark comparison of Phi-3.5-MoE across 16 evaluations.
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmar.
Full Phi-3.5-MoE specs →| Benchmark | Phi-3.5-MoE |
|---|---|
| code | |
| HumanEval | 70.7% |
| MBPP | 80.8% |
| general | |
| ArenaHard | 37.9% |
| MMLU | 78.9% |
| MMLU-Pro | 54.3% |
| MMMLU | 69.9% |
| knowledge | |
| ARC-Challenge | 91 |
| math | |
| GSM8K | 88.7% |
| MATH | 59.5% |
| MGSM | 58.7 |
| reasoning | |
| BIG-Bench Hard | 79.1% |
| GPQA Diamond | 36.8% |
| HellaSwag | 83.8 |
| PIQA | 88.6 |
| Winogrande | 81.3 |
| safety | |
| TruthfulQA | 77.5% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Phi-3.5-MoE has the strongest result on HumanEval among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 16 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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