Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 3/8 categoriesLlama 4 Scout
44
0/8 categoriesGemma 4 31B· Llama 4 Scout
Pick Gemma 4 31B if you want the stronger benchmark profile. Llama 4 Scout only becomes the better choice if you need the larger 10M context window 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 44. 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 reasoning, where it averages 66.4 against 43. The single biggest benchmark swing on the page is MMLU-Pro, 85.2% to 51%.
Gemma 4 31B is the reasoning model in the pair, while Llama 4 Scout 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. Llama 4 Scout gives you the larger context window at 10M, compared with 256K for Gemma 4 31B.
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 | Llama 4 Scout |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 39% |
| Coding | ||
| LiveCodeBench | 80% | — |
| HumanEval | — | 39% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 60% |
| OfficeQA Pro | — | 55% |
| ReasoningGemma 4 31B wins | ||
| BBH | 74.4% | 60% |
| MRCRv2 | 66.4% | — |
| MuSR | — | 43% |
| KnowledgeGemma 4 31B wins | ||
| GPQA | 84.3% | — |
| MMLU-Pro | 85.2% | 51% |
| HLE | 26.5% | — |
| HLE w/o tools | 19.5% | — |
| SuperGPQA | — | 44% |
| SimpleQA | — | 45% |
| Instruction Following | ||
| IFEval | — | 68% |
| Multilingual | ||
| MMLU-ProX | — | 58% |
| Mathematics | ||
| AIME 2025 | — | 48% |
| HMMT Feb 2023 | — | 43% |
| HMMT Feb 2024 | — | 45% |
| HMMT Feb 2025 | — | 44% |
| BRUMO 2025 | — | 46% |
Gemma 4 31B is ahead overall, 73 to 44. The biggest single separator in this matchup is MMLU-Pro, where the scores are 85.2% and 51%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 47.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for reasoning in this comparison, averaging 66.4 versus 43. 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 57.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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