Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1 (Reasoning)
43
Winner · 2/8 categoriesGranite-4.0-1B
~40
1/8 categoriesDeepSeek V3.1 (Reasoning)· Granite-4.0-1B
Pick DeepSeek V3.1 (Reasoning) if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice 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) 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.
DeepSeek V3.1 (Reasoning)'s sharpest advantage is in multilingual, where it averages 62.1 against 27.5. The single biggest benchmark swing on the page is HumanEval, 26% to 73%. Granite-4.0-1B does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
DeepSeek V3.1 (Reasoning) is the reasoning model in the pair, while Granite-4.0-1B 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-4.0-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 42% | — |
| BrowseComp | 48% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 26% | 73% |
| SWE-bench Verified | 14% | — |
| LiveCodeBench | 16% | — |
| SWE-bench Pro | 25% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 37% | — |
| OfficeQA Pro | 47% | — |
| Reasoning | ||
| MuSR | 30% | — |
| BBH | 64% | 59.7% |
| LongBench v2 | 57% | — |
| MRCRv2 | 56% | — |
| KnowledgeDeepSeek V3.1 (Reasoning) wins | ||
| MMLU | 34% | 59.7% |
| GPQA | 33% | 29.7% |
| SuperGPQA | 31% | — |
| MMLU-Pro | 53% | 32.9% |
| HLE | 10% | — |
| FrontierScience | 37% | — |
| SimpleQA | 32% | — |
| Instruction FollowingGranite-4.0-1B wins | ||
| IFEval | 70% | 78.5% |
| MultilingualDeepSeek V3.1 (Reasoning) wins | ||
| MGSM | 64% | 27.5% |
| 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) is ahead overall, 43 to 40. The biggest single separator in this matchup is HumanEval, where the scores are 26% and 73%.
DeepSeek V3.1 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 32.5 versus 31.7. 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 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 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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