Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-350M
~27
0/8 categoriesLlama 4 Maverick
39
Winner · 3/8 categoriesGranite-4.0-350M· Llama 4 Maverick
Pick Llama 4 Maverick if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Llama 4 Maverick is clearly ahead on the aggregate, 39 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Llama 4 Maverick's sharpest advantage is in multilingual, where it averages 59.8 against 16.2. The single biggest benchmark swing on the page is MGSM, 16.2% to 63%.
Llama 4 Maverick gives you the larger context window at 1M, 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 | Llama 4 Maverick |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| BrowseComp | — | 51% |
| OSWorld-Verified | — | 38% |
| Coding | ||
| HumanEval | 38% | 38% |
| SWE-bench Verified | — | 13% |
| LiveCodeBench | — | 15% |
| SWE-bench Pro | — | 17% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 59% |
| OfficeQA Pro | — | 54% |
| Reasoning | ||
| BBH | 33.3% | 63% |
| MuSR | — | 42% |
| LongBench v2 | — | 63% |
| MRCRv2 | — | 63% |
| KnowledgeLlama 4 Maverick wins | ||
| MMLU | 36.2% | 46% |
| GPQA | 26.1% | 45% |
| MMLU-Pro | 14.4% | 53% |
| SuperGPQA | — | 43% |
| HLE | — | 4% |
| FrontierScience | — | 45% |
| SimpleQA | — | 44% |
| Instruction FollowingLlama 4 Maverick wins | ||
| IFEval | 61.6% | 68% |
| MultilingualLlama 4 Maverick wins | ||
| MGSM | 16.2% | 63% |
| MMLU-ProX | — | 58% |
| Mathematics | ||
| AIME 2023 | — | 46% |
| AIME 2024 | — | 48% |
| AIME 2025 | — | 47% |
| HMMT Feb 2023 | — | 42% |
| HMMT Feb 2024 | — | 44% |
| HMMT Feb 2025 | — | 43% |
| BRUMO 2025 | — | 45% |
| MATH-500 | — | 59% |
Llama 4 Maverick is ahead overall, 39 to 27. The biggest single separator in this matchup is MGSM, where the scores are 16.2% and 63%.
Llama 4 Maverick has the edge for knowledge tasks in this comparison, averaging 37 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for instruction following in this comparison, averaging 68 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for multilingual tasks in this comparison, averaging 59.8 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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