Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 4/8 categoriesNemotron 3 Nano 30B
42
0/8 categoriesGemma 4 31B· Nemotron 3 Nano 30B
Pick Gemma 4 31B if you want the stronger benchmark profile. Nemotron 3 Nano 30B 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 42. 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 22.5. The single biggest benchmark swing on the page is LiveCodeBench, 80% to 16%.
Gemma 4 31B is the reasoning model in the pair, while Nemotron 3 Nano 30B 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 32K for Nemotron 3 Nano 30B.
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 | Nemotron 3 Nano 30B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 38% |
| BrowseComp | — | 43% |
| OSWorld-Verified | — | 39% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | 16% |
| HumanEval | — | 49% |
| SWE-bench Verified | — | 26% |
| SWE-bench Pro | — | 27% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 38% |
| OfficeQA Pro | — | 54% |
| ReasoningGemma 4 31B wins | ||
| BBH | 74.4% | 72% |
| MRCRv2 | 66.4% | 51% |
| MuSR | — | 52% |
| LongBench v2 | — | 51% |
| KnowledgeGemma 4 31B wins | ||
| GPQA | 84.3% | 56% |
| MMLU-Pro | 85.2% | 65% |
| HLE | 26.5% | 1% |
| HLE w/o tools | 19.5% | — |
| MMLU | — | 57% |
| SuperGPQA | — | 54% |
| FrontierScience | — | 54% |
| SimpleQA | — | 54% |
| Instruction Following | ||
| IFEval | — | 78% |
| Multilingual | ||
| MGSM | — | 75% |
| MMLU-ProX | — | 70% |
| Mathematics | ||
| AIME 2023 | — | 57% |
| AIME 2024 | — | 59% |
| HMMT Feb 2024 | — | 55% |
| HMMT Feb 2025 | — | 54% |
| BRUMO 2025 | — | 56% |
| MATH-500 | — | 73% |
Gemma 4 31B is ahead overall, 73 to 42. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80% and 16%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 44.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 22.5. Inside this category, LiveCodeBench 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 51.3. 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 45.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.