Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 2/8 categoriesQwen2.5-72B
60
2/8 categoriesGemma 4 31B· Qwen2.5-72B
Pick Gemma 4 31B if you want the stronger benchmark profile. Qwen2.5-72B 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 31B is clearly ahead on the aggregate, 73 to 60. 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 44.1. The single biggest benchmark swing on the page is LiveCodeBench, 80% to 40%. Qwen2.5-72B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 31B is the reasoning model in the pair, while Qwen2.5-72B 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 Qwen2.5-72B.
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 | Gemma 4 31B | Qwen2.5-72B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 56% |
| BrowseComp | — | 64% |
| OSWorld-Verified | — | 55% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | 40% |
| HumanEval | — | 75% |
| SWE-bench Verified | — | 46% |
| SWE-bench Pro | — | 47% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 64% |
| OfficeQA Pro | — | 70% |
| ReasoningQwen2.5-72B wins | ||
| BBH | 74.4% | 81% |
| MRCRv2 | 66.4% | 71% |
| MuSR | — | 78% |
| LongBench v2 | — | 72% |
| KnowledgeQwen2.5-72B wins | ||
| GPQA | 84.3% | 82% |
| MMLU-Pro | 85.2% | 75% |
| HLE | 26.5% | 11% |
| HLE w/o tools | 19.5% | — |
| MMLU | — | 83% |
| SuperGPQA | — | 80% |
| FrontierScience | — | 70% |
| SimpleQA | — | 80% |
| Instruction Following | ||
| IFEval | — | 85% |
| Multilingual | ||
| MGSM | — | 84% |
| MMLU-ProX | — | 79% |
| Mathematics | ||
| AIME 2023 | — | 84% |
| AIME 2024 | — | 86% |
| AIME 2025 | — | 85% |
| HMMT Feb 2023 | — | 80% |
| HMMT Feb 2024 | — | 82% |
| HMMT Feb 2025 | — | 81% |
| BRUMO 2025 | — | 83% |
| MATH-500 | — | 84% |
Gemma 4 31B is ahead overall, 73 to 60. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80% and 40%.
Qwen2.5-72B has the edge for knowledge tasks in this comparison, averaging 61.5 versus 61.3. 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 44.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for reasoning in this comparison, averaging 73.3 versus 66.4. Inside this category, BBH 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 66.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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