Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
Winner · 1/8 categoriesLlama 4 Behemoth
35
2/8 categoriesGranite-4.0-1B· Llama 4 Behemoth
Pick Granite-4.0-1B if you want the stronger benchmark profile. Llama 4 Behemoth only becomes the better choice if multilingual is the priority.
Granite-4.0-1B is clearly ahead on the aggregate, 40 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Granite-4.0-1B's sharpest advantage is in instruction following, where it averages 78.5 against 68. The single biggest benchmark swing on the page is MGSM, 27.5% to 66%. Llama 4 Behemoth does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Granite-4.0-1B gives you the larger context window at 128K, compared with 32K for Llama 4 Behemoth.
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-4.0-1B | Llama 4 Behemoth |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 33% |
| BrowseComp | — | 38% |
| OSWorld-Verified | — | 34% |
| Coding | ||
| HumanEval | 73% | 40% |
| SWE-bench Verified | — | 15% |
| LiveCodeBench | — | 13% |
| SWE-bench Pro | — | 15% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 60% |
| OfficeQA Pro | — | 49% |
| Reasoning | ||
| BBH | 59.7% | 62% |
| MuSR | — | 44% |
| LongBench v2 | — | 46% |
| MRCRv2 | — | 46% |
| KnowledgeLlama 4 Behemoth wins | ||
| MMLU | 59.7% | 48% |
| GPQA | 29.7% | 47% |
| MMLU-Pro | 32.9% | 54% |
| SuperGPQA | — | 45% |
| HLE | — | 3% |
| FrontierScience | — | 43% |
| SimpleQA | — | 46% |
| Instruction FollowingGranite-4.0-1B wins | ||
| IFEval | 78.5% | 68% |
| MultilingualLlama 4 Behemoth wins | ||
| MGSM | 27.5% | 66% |
| MMLU-ProX | — | 61% |
| Mathematics | ||
| AIME 2023 | — | 48% |
| AIME 2024 | — | 50% |
| AIME 2025 | — | 49% |
| HMMT Feb 2023 | — | 44% |
| HMMT Feb 2024 | — | 46% |
| HMMT Feb 2025 | — | 45% |
| BRUMO 2025 | — | 47% |
| MATH-500 | — | 60% |
Granite-4.0-1B is ahead overall, 40 to 35. The biggest single separator in this matchup is MGSM, where the scores are 27.5% and 66%.
Llama 4 Behemoth has the edge for knowledge tasks in this comparison, averaging 37.3 versus 31.7. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Granite-4.0-1B has the edge for instruction following in this comparison, averaging 78.5 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for multilingual tasks in this comparison, averaging 62.8 versus 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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