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A head-to-head benchmark comparison of dots.llm1 across 19 evaluations.
Mixture of Experts (MoE) models have emerged as a promising paradigm for scaling language models efficiently by activating only a subset of parameters for each .
Full dots.llm1 specs →| Benchmark | dots.llm1 |
|---|---|
| code | |
| HumanEval | 88.4% |
| LiveCodeBench | 32% |
| MBPP+ | 74.5% |
| general | |
| AlpacaEval 2 | 64.4% |
| ArenaHard | 87.1% |
| C-Eval | 92.2% |
| CLUEWSC | 92.6% |
| IFEval | 82.1% |
| MMLU | 82.1% |
| MMLU-Pro | 70.4% |
| MMLU-Redux | 85.1% |
| math | |
| AIME 2024 | 33.1% |
| CNMO 2024 | 40.6% |
| MATH | 85% |
| MATH-500 | 84.8% |
| reasoning | |
| DROP | 87% |
| GPQA Diamond | 52.6% |
| safety | |
| C-SimpleQA | 56.7% |
| SimpleQA | 9.3% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
dots.llm1 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 19 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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