Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1
40
1/8 categoriesGranite-4.0-1B
~40
2/8 categoriesDeepSeek V3.1· Granite-4.0-1B
Treat this as a split decision. DeepSeek V3.1 makes more sense if multilingual is the priority; Granite-4.0-1B is the better fit if instruction following is the priority.
DeepSeek V3.1 and Granite-4.0-1B 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.
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-4.0-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 39% | — |
| OSWorld-Verified | 33% | — |
| Coding | ||
| HumanEval | 25% | 73% |
| SWE-bench Verified | 13% | — |
| LiveCodeBench | 15% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 35% | — |
| OfficeQA Pro | 45% | — |
| Reasoning | ||
| MuSR | 29% | — |
| BBH | 61% | 59.7% |
| MRCRv2 | 48% | — |
| KnowledgeGranite-4.0-1B wins | ||
| MMLU | 33% | 59.7% |
| GPQA | 32% | 29.7% |
| SuperGPQA | 30% | — |
| MMLU-Pro | 53% | 32.9% |
| HLE | 2% | — |
| FrontierScience | 37% | — |
| SimpleQA | 31% | — |
| Instruction FollowingGranite-4.0-1B wins | ||
| IFEval | 67% | 78.5% |
| MultilingualDeepSeek V3.1 wins | ||
| MGSM | 64% | 27.5% |
| 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% | — |
DeepSeek V3.1 and Granite-4.0-1B are tied on overall score, so the right pick depends on which category matters most for your use case.
Granite-4.0-1B has the edge for knowledge tasks in this comparison, averaging 31.7 versus 30.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Granite-4.0-1B has the edge for instruction following in this comparison, averaging 78.5 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 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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