Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
1/8 categoriesLlama 3 70B
45
Winner · 2/8 categoriesGranite-4.0-H-1B· Llama 3 70B
Pick Llama 3 70B if you want the stronger benchmark profile. Granite-4.0-H-1B only becomes the better choice if instruction following is the priority.
Llama 3 70B has the cleaner overall profile here, landing at 45 versus 43. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Llama 3 70B's sharpest advantage is in multilingual, where it averages 67.5 against 37.8. The single biggest benchmark swing on the page is MGSM, 37.8% to 72%. Granite-4.0-H-1B does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
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 3 70B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| OSWorld-Verified | — | 41% |
| Coding | ||
| HumanEval | 74% | 50% |
| SWE-bench Verified | — | 9% |
| LiveCodeBench | — | 19% |
| SWE-bench Pro | — | 14% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 50% |
| Reasoning | ||
| BBH | 60.4% | 74% |
| MuSR | — | 54% |
| LongBench v2 | — | 61% |
| MRCRv2 | — | 61% |
| KnowledgeLlama 3 70B wins | ||
| MMLU | 59.4% | 58% |
| GPQA | 29.9% | 58% |
| MMLU-Pro | 34.0% | 55% |
| SuperGPQA | — | 56% |
| FrontierScience | — | 54% |
| SimpleQA | — | 56% |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 77.4% | 77% |
| MultilingualLlama 3 70B wins | ||
| MGSM | 37.8% | 72% |
| MMLU-ProX | — | 65% |
| Mathematics | ||
| AIME 2023 | — | 58% |
| AIME 2024 | — | 60% |
| AIME 2025 | — | 59% |
| HMMT Feb 2023 | — | 54% |
| HMMT Feb 2024 | — | 56% |
| HMMT Feb 2025 | — | 55% |
| BRUMO 2025 | — | 57% |
| MATH-500 | — | 71% |
Llama 3 70B is ahead overall, 45 to 43. The biggest single separator in this matchup is MGSM, where the scores are 37.8% and 72%.
Llama 3 70B has the edge for knowledge tasks in this comparison, averaging 55.6 versus 32.6. Inside this category, GPQA 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.
Llama 3 70B has the edge for multilingual tasks in this comparison, averaging 67.5 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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