Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Holo3-35B-A3B
~78
Winner · 1/8 categoriesKimi K2
53
0/8 categoriesHolo3-35B-A3B· Kimi K2
Pick Holo3-35B-A3B if you want the stronger benchmark profile. Kimi K2 only becomes the better choice if you need the larger 128K context window.
Holo3-35B-A3B is clearly ahead on the aggregate, 78 to 53. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-35B-A3B's sharpest advantage is in agentic, where it averages 77.8 against 52.1.
Kimi K2 gives you the larger context window at 128K, compared with 64K for Holo3-35B-A3B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Holo3-35B-A3B | Kimi K2 |
|---|---|---|
| AgenticHolo3-35B-A3B wins | ||
| OSWorld-Verified | 77.8% | — |
| Terminal-Bench 2.0 | — | 47.1% |
| BrowseComp | — | 60.2% |
| tau2-bench | — | 66.1% |
| Coding | ||
| SWE-bench Verified | — | 65.8% |
| LiveCodeBench | — | 53.7% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| hle | — | 44.9% |
| Knowledge | ||
| MMLU | — | 89.5% |
| GPQA | — | 75.1% |
| SuperGPQA | — | 57.2% |
| MMLU-Pro | — | 81.1% |
| SimpleQA | — | 31% |
| Instruction Following | ||
| IFEval | — | 89.8% |
| Multilingual | ||
| sweMultilingual | — | 61.1% |
| Mathematics | ||
| AIME 2024 | — | 69.6% |
| AIME 2025 | — | 49.5% |
| MATH-500 | — | 97.4% |
| HMMT Feb 2025 | — | 38.8% |
Holo3-35B-A3B is ahead overall, 78 to 53.
Holo3-35B-A3B has the edge for agentic tasks in this comparison, averaging 77.8 versus 52.1. Kimi K2 stays close enough that the answer can still flip depending on your workload.
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