Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
Winner · 1/8 categoriesLlama 4 Maverick
39
2/8 categoriesGranite-4.0-H-1B· Llama 4 Maverick
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Llama 4 Maverick only becomes the better choice if multilingual is the priority or you need the larger 1M context window.
Granite-4.0-H-1B is clearly ahead on the aggregate, 43 to 39. 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 68. The single biggest benchmark swing on the page is HumanEval, 74% to 38%. Llama 4 Maverick does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Llama 4 Maverick gives you the larger context window at 1M, compared with 128K for Granite-4.0-H-1B.
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 | Llama 4 Maverick |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| BrowseComp | — | 51% |
| OSWorld-Verified | — | 38% |
| Coding | ||
| HumanEval | 74% | 38% |
| SWE-bench Verified | — | 13% |
| LiveCodeBench | — | 15% |
| SWE-bench Pro | — | 17% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 59% |
| OfficeQA Pro | — | 54% |
| Reasoning | ||
| BBH | 60.4% | 63% |
| MuSR | — | 42% |
| LongBench v2 | — | 63% |
| MRCRv2 | — | 63% |
| KnowledgeLlama 4 Maverick wins | ||
| MMLU | 59.4% | 46% |
| GPQA | 29.9% | 45% |
| MMLU-Pro | 34.0% | 53% |
| SuperGPQA | — | 43% |
| HLE | — | 4% |
| FrontierScience | — | 45% |
| SimpleQA | — | 44% |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 77.4% | 68% |
| MultilingualLlama 4 Maverick wins | ||
| MGSM | 37.8% | 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% |
Granite-4.0-H-1B is ahead overall, 43 to 39. The biggest single separator in this matchup is HumanEval, where the scores are 74% and 38%.
Llama 4 Maverick has the edge for knowledge tasks in this comparison, averaging 37 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 68. 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 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.