Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
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1/8 categoriesMoonshot v1
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2/8 categoriesGranite-4.0-H-1B· Moonshot v1
Treat this as a split decision. Granite-4.0-H-1B makes more sense if instruction following is the priority; Moonshot v1 is the better fit if multilingual is the priority.
Granite-4.0-H-1B and Moonshot v1 finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
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-H-1B | Moonshot v1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 39% |
| BrowseComp | — | 49% |
| Coding | ||
| HumanEval | 74% | 45% |
| SWE-bench Verified | — | 34% |
| LiveCodeBench | — | 21% |
| SWE-bench Pro | — | 30% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 49% |
| OfficeQA Pro | — | 57% |
| Reasoning | ||
| BBH | 60.4% | 73% |
| MuSR | — | 49% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 56% |
| KnowledgeMoonshot v1 wins | ||
| MMLU | 59.4% | 53% |
| GPQA | 29.9% | 52% |
| MMLU-Pro | 34.0% | 64% |
| SuperGPQA | — | 50% |
| HLE | — | 5% |
| FrontierScience | — | 49% |
| SimpleQA | — | 51% |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 77.4% | 77% |
| MultilingualMoonshot v1 wins | ||
| MGSM | 37.8% | 73% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| AIME 2023 | — | 53% |
| AIME 2024 | — | 55% |
| AIME 2025 | — | 54% |
| HMMT Feb 2023 | — | 49% |
| HMMT Feb 2024 | — | 51% |
| HMMT Feb 2025 | — | 50% |
| BRUMO 2025 | — | 52% |
Granite-4.0-H-1B and Moonshot v1 are tied on overall score, so the right pick depends on which category matters most for your use case.
Moonshot v1 has the edge for knowledge tasks in this comparison, averaging 42.9 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 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Moonshot v1 has the edge for multilingual tasks in this comparison, averaging 69.8 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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