Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1
40
1/8 categoriesGranite-4.0-H-1B
~43
Winner · 2/8 categoriesDeepSeek V3.1· Granite-4.0-H-1B
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. DeepSeek V3.1 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's sharpest advantage is in instruction following, where it averages 77.4 against 67. The single biggest benchmark swing on the page is HumanEval, 25% to 74%. DeepSeek V3.1 does hit back in multilingual, 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 | DeepSeek V3.1 | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 39% | — |
| OSWorld-Verified | 33% | — |
| Coding | ||
| HumanEval | 25% | 74% |
| SWE-bench Verified | 13% | — |
| LiveCodeBench | 15% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 35% | — |
| OfficeQA Pro | 45% | — |
| Reasoning | ||
| MuSR | 29% | — |
| BBH | 61% | 60.4% |
| MRCRv2 | 48% | — |
| KnowledgeGranite-4.0-H-1B wins | ||
| MMLU | 33% | 59.4% |
| GPQA | 32% | 29.9% |
| SuperGPQA | 30% | — |
| MMLU-Pro | 53% | 34.0% |
| HLE | 2% | — |
| FrontierScience | 37% | — |
| SimpleQA | 31% | — |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 67% | 77.4% |
| MultilingualDeepSeek V3.1 wins | ||
| MGSM | 64% | 37.8% |
| MMLU-ProX | 59% | — |
| Mathematics | ||
| AIME 2023 | 33% | — |
| AIME 2024 | 35% | — |
| AIME 2025 | 34% | — |
| HMMT Feb 2023 | 29% | — |
| HMMT Feb 2024 | 31% | — |
| HMMT Feb 2025 | 30% | — |
| BRUMO 2025 | 32% | — |
| MATH-500 | 59% | — |
Granite-4.0-H-1B is ahead overall, 43 to 40. The biggest single separator in this matchup is HumanEval, where the scores are 25% and 74%.
Granite-4.0-H-1B has the edge for knowledge tasks in this comparison, averaging 32.6 versus 30.3. Inside this category, MMLU 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.1 has the edge for multilingual tasks in this comparison, averaging 60.8 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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