Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
Winner · 0/8 categoriesNemotron-4 15B
40
2/8 categoriesGranite-4.0-H-1B· Nemotron-4 15B
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Nemotron-4 15B only becomes the better choice if multilingual is the priority.
Granite-4.0-H-1B has the cleaner overall profile here, landing at 43 versus 40. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Granite-4.0-H-1B gives you the larger context window at 128K, compared with 32K for Nemotron-4 15B.
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 | Nemotron-4 15B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| BrowseComp | — | 47% |
| OSWorld-Verified | — | 42% |
| Coding | ||
| HumanEval | 74% | 46% |
| SWE-bench Verified | — | 31% |
| LiveCodeBench | — | 22% |
| SWE-bench Pro | — | 30% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 46% |
| OfficeQA Pro | — | 54% |
| Reasoning | ||
| BBH | 60.4% | 73% |
| MuSR | — | 50% |
| LongBench v2 | — | 52% |
| MRCRv2 | — | 51% |
| KnowledgeNemotron-4 15B wins | ||
| MMLU | 59.4% | 54% |
| GPQA | 29.9% | 53% |
| MMLU-Pro | 34.0% | — |
| SuperGPQA | — | 51% |
| HLE | — | 5% |
| FrontierScience | — | 50% |
| SimpleQA | — | 52% |
| Instruction Following | ||
| IFEval | 77.4% | — |
| MultilingualNemotron-4 15B wins | ||
| MGSM | 37.8% | 75% |
| MMLU-ProX | — | 71% |
| Mathematics | ||
| AIME 2023 | — | 54% |
| AIME 2024 | — | 56% |
| AIME 2025 | — | 55% |
| HMMT Feb 2023 | — | 50% |
| HMMT Feb 2024 | — | 52% |
| HMMT Feb 2025 | — | 51% |
| BRUMO 2025 | — | 53% |
Granite-4.0-H-1B is ahead overall, 43 to 40. The biggest single separator in this matchup is MGSM, where the scores are 37.8% and 75%.
Nemotron-4 15B has the edge for knowledge tasks in this comparison, averaging 37.7 versus 32.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Nemotron-4 15B has the edge for multilingual tasks in this comparison, averaging 72.4 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.