Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 3 27B
34
2/8 categoriesGranite-4.0-H-1B
~43
Winner · 1/8 categoriesGemma 3 27B· Granite-4.0-H-1B
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Gemma 3 27B only becomes the better choice if multilingual is the priority.
Granite-4.0-H-1B is clearly ahead on the aggregate, 43 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Granite-4.0-H-1B's sharpest advantage is in instruction following, where it averages 77.4 against 67. The single biggest benchmark swing on the page is HumanEval, 37% to 74%. Gemma 3 27B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Granite-4.0-H-1B gives you the larger context window at 128K, compared with 32K for Gemma 3 27B.
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 | Gemma 3 27B | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 42% | — |
| OSWorld-Verified | 35% | — |
| Coding | ||
| HumanEval | 37% | 74% |
| SWE-bench Verified | 16% | — |
| LiveCodeBench | 15% | — |
| SWE-bench Pro | 17% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 39% | — |
| OfficeQA Pro | 45% | — |
| Reasoning | ||
| MuSR | 41% | — |
| BBH | 62% | 60.4% |
| LongBench v2 | 47% | — |
| MRCRv2 | 44% | — |
| KnowledgeGemma 3 27B wins | ||
| MMLU | 45% | 59.4% |
| GPQA | 44% | 29.9% |
| SuperGPQA | 42% | — |
| MMLU-Pro | 50% | 34.0% |
| HLE | 3% | — |
| FrontierScience | 42% | — |
| SimpleQA | 43% | — |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 67% | 77.4% |
| MultilingualGemma 3 27B wins | ||
| MGSM | 64% | 37.8% |
| MMLU-ProX | 60% | — |
| Mathematics | ||
| AIME 2023 | 45% | — |
| AIME 2024 | 47% | — |
| AIME 2025 | 46% | — |
| HMMT Feb 2023 | 41% | — |
| HMMT Feb 2024 | 43% | — |
| HMMT Feb 2025 | 42% | — |
| BRUMO 2025 | 44% | — |
| MATH-500 | 56% | — |
Granite-4.0-H-1B is ahead overall, 43 to 34. The biggest single separator in this matchup is HumanEval, where the scores are 37% and 74%.
Gemma 3 27B has the edge for knowledge tasks in this comparison, averaging 35.2 versus 32.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Granite-4.0-H-1B has the edge for instruction following in this comparison, averaging 77.4 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemma 3 27B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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