Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2
61
1/8 categoriesGemma 4 26B A4B
64
Winner · 3/8 categoriesDeepSeek V3.2· Gemma 4 26B A4B
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 26B A4B has the cleaner overall profile here, landing at 64 versus 61. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemma 4 26B A4B's sharpest advantage is in coding, where it averages 77.1 against 56.1. The single biggest benchmark swing on the page is MRCRv2, 70% to 44.1%. DeepSeek V3.2 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 26B A4B 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 26B A4B 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 26B A4B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 60% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 55% | — |
| Claw-Eval | 51.0% | — |
| DeepPlanning | 19.0% | — |
| VITA-Bench | 18.5% | — |
| CodingGemma 4 26B A4B wins | ||
| HumanEval | 76% | — |
| SWE-bench Verified | 45% | — |
| SWE-Rebench | 60.9% | — |
| React Native Evals | 69% | — |
| LiveCodeBench | — | 77.1% |
| Multimodal & GroundedGemma 4 26B A4B wins | ||
| MMMU-Pro | 61% | 73.8% |
| OfficeQA Pro | 72% | — |
| ReasoningDeepSeek V3.2 wins | ||
| LongBench v2 | 69% | — |
| MRCRv2 | 70% | 44.1% |
| ARC-AGI-2 | 4% | — |
| BBH | — | 64.8% |
| KnowledgeGemma 4 26B A4B wins | ||
| GPQA | 83% | 82.3% |
| HLE | 11% | 17.2% |
| FrontierScience | 72% | — |
| MMLU-Pro | — | 82.6% |
| HLE w/o tools | — | 8.7% |
| 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 26B A4B is ahead overall, 64 to 61. The biggest single separator in this matchup is MRCRv2, where the scores are 70% and 44.1%.
Gemma 4 26B A4B has the edge for knowledge tasks in this comparison, averaging 56.1 versus 48. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for coding in this comparison, averaging 77.1 versus 56.1. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
DeepSeek V3.2 has the edge for reasoning in this comparison, averaging 49 versus 44.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for multimodal and grounded tasks in this comparison, averaging 73.8 versus 66. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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