Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-350M
~27
0/8 categoriesKimi K2.5 (Reasoning)
76
Winner · 3/8 categoriesGranite-4.0-350M· Kimi K2.5 (Reasoning)
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in multilingual, where it averages 89.5 against 16.2. The single biggest benchmark swing on the page is MGSM, 16.2% to 96%.
Kimi K2.5 (Reasoning) is the reasoning model in the pair, while Granite-4.0-350M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Kimi K2.5 (Reasoning) gives you the larger context window at 128K, compared with 32K for Granite-4.0-350M.
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 | Granite-4.0-350M | Kimi K2.5 (Reasoning) |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 50.8% |
| BrowseComp | — | 60.6% |
| OSWorld-Verified | — | 63.3% |
| Coding | ||
| HumanEval | 38% | 99% |
| SWE-bench Verified | — | 76.8% |
| LiveCodeBench | — | 85% |
| SWE-bench Pro | — | 70% |
| SWE-Rebench | — | 57.4% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 78.5% |
| OfficeQA Pro | — | 77% |
| Reasoning | ||
| BBH | 33.3% | 91% |
| MuSR | — | 86% |
| LongBench v2 | — | 61% |
| MRCRv2 | — | 81% |
| KnowledgeKimi K2.5 (Reasoning) wins | ||
| MMLU | 36.2% | 92% |
| GPQA | 26.1% | 87.6% |
| MMLU-Pro | 14.4% | 87.1% |
| SuperGPQA | — | 88% |
| HLE | — | 27% |
| FrontierScience | — | 80% |
| SimpleQA | — | 54% |
| Instruction FollowingKimi K2.5 (Reasoning) wins | ||
| IFEval | 61.6% | 94% |
| MultilingualKimi K2.5 (Reasoning) wins | ||
| MGSM | 16.2% | 96% |
| MMLU-ProX | — | 86% |
| Mathematics | ||
| AIME 2023 | — | 94% |
| AIME 2024 | — | 96% |
| AIME 2025 | — | 96.1% |
| HMMT Feb 2023 | — | 90% |
| HMMT Feb 2024 | — | 92% |
| HMMT Feb 2025 | — | 95.4% |
| BRUMO 2025 | — | 93% |
| MATH-500 | — | 92% |
Kimi K2.5 (Reasoning) is ahead overall, 76 to 27. The biggest single separator in this matchup is MGSM, where the scores are 16.2% and 96%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 67.9 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for instruction following in this comparison, averaging 94 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 89.5 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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