Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1 (Reasoning)
43
1/8 categoriesGranite-4.0-H-1B
~43
2/8 categoriesDeepSeek V3.1 (Reasoning)· Granite-4.0-H-1B
Treat this as a split decision. DeepSeek V3.1 (Reasoning) makes more sense if multilingual is the priority or you want the stronger reasoning-first profile; Granite-4.0-H-1B is the better fit if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V3.1 (Reasoning) and Granite-4.0-H-1B finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
DeepSeek V3.1 (Reasoning) is the reasoning model in the pair, while Granite-4.0-H-1B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
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 (Reasoning) | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 42% | — |
| BrowseComp | 48% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 26% | 74% |
| SWE-bench Verified | 14% | — |
| LiveCodeBench | 16% | — |
| SWE-bench Pro | 25% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 37% | — |
| OfficeQA Pro | 47% | — |
| Reasoning | ||
| MuSR | 30% | — |
| BBH | 64% | 60.4% |
| LongBench v2 | 57% | — |
| MRCRv2 | 56% | — |
| KnowledgeGranite-4.0-H-1B wins | ||
| MMLU | 34% | 59.4% |
| GPQA | 33% | 29.9% |
| SuperGPQA | 31% | — |
| MMLU-Pro | 53% | 34.0% |
| HLE | 10% | — |
| FrontierScience | 37% | — |
| SimpleQA | 32% | — |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 70% | 77.4% |
| MultilingualDeepSeek V3.1 (Reasoning) wins | ||
| MGSM | 64% | 37.8% |
| MMLU-ProX | 61% | — |
| Mathematics | ||
| AIME 2023 | 34% | — |
| AIME 2024 | 36% | — |
| AIME 2025 | 35% | — |
| HMMT Feb 2023 | 30% | — |
| HMMT Feb 2024 | 32% | — |
| HMMT Feb 2025 | 31% | — |
| BRUMO 2025 | 33% | — |
| MATH-500 | 62% | — |
DeepSeek V3.1 (Reasoning) and Granite-4.0-H-1B are tied on overall score, so the right pick depends on which category matters most for your use case.
Granite-4.0-H-1B has the edge for knowledge tasks in this comparison, averaging 32.6 versus 32.5. 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 70. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 62.1 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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