Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2
61
0/8 categoriesGemma 4 31B
73
Winner · 4/8 categoriesDeepSeek V3.2· Gemma 4 31B
Pick Gemma 4 31B if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the aggregate, 73 to 61. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B's sharpest advantage is in coding, where it averages 80 against 56.1. The single biggest benchmark swing on the page is MMMU-Pro, 61% to 76.9%.
Gemma 4 31B is the reasoning model in the pair, while DeepSeek V3.2 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. Gemma 4 31B gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
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 V3.2 | Gemma 4 31B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 60% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 55% | — |
| Claw-Eval | 51.0% | — |
| DeepPlanning | 19.0% | — |
| VITA-Bench | 18.5% | — |
| CodingGemma 4 31B wins | ||
| HumanEval | 76% | — |
| SWE-bench Verified | 45% | — |
| SWE-Rebench | 60.9% | — |
| React Native Evals | 69% | — |
| LiveCodeBench | — | 80% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 61% | 76.9% |
| OfficeQA Pro | 72% | — |
| ReasoningGemma 4 31B wins | ||
| LongBench v2 | 69% | — |
| MRCRv2 | 70% | 66.4% |
| ARC-AGI-2 | 4% | — |
| BBH | — | 74.4% |
| KnowledgeGemma 4 31B wins | ||
| GPQA | 83% | 84.3% |
| HLE | 11% | 26.5% |
| FrontierScience | 72% | — |
| MMLU-Pro | — | 85.2% |
| HLE w/o tools | — | 19.5% |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| MGSM | 84% | — |
| MMLU-ProX | 81% | — |
| Mathematics | ||
| AIME 2023 | 84% | — |
| AIME 2024 | 86% | — |
| AIME 2025 | 85% | — |
| HMMT Feb 2023 | 80% | — |
| MATH-500 | 81% | — |
Gemma 4 31B is ahead overall, 73 to 61. The biggest single separator in this matchup is MMMU-Pro, where the scores are 61% and 76.9%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 48. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 56.1. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
Gemma 4 31B has the edge for reasoning in this comparison, averaging 66.4 versus 49. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 66. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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