Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
1/8 categoriesGemma 4 31B
73
Winner · 3/8 categoriesDeepSeek Coder 2.0· Gemma 4 31B
Pick Gemma 4 31B if you want the stronger benchmark profile. DeepSeek Coder 2.0 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 62. 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 52.5. The single biggest benchmark swing on the page is LiveCodeBench, 45% to 80%. DeepSeek Coder 2.0 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B is the reasoning model in the pair, while DeepSeek Coder 2.0 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 Coder 2.0.
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 Coder 2.0 | Gemma 4 31B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 73% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 65% | — |
| CodingGemma 4 31B wins | ||
| HumanEval | 82% | — |
| SWE-bench Verified | 51% | — |
| LiveCodeBench | 45% | 80% |
| SWE-bench Pro | 61% | — |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 50% | 76.9% |
| OfficeQA Pro | 69% | — |
| ReasoningDeepSeek Coder 2.0 wins | ||
| MuSR | 76% | — |
| BBH | 84% | 74.4% |
| LongBench v2 | 73% | — |
| MRCRv2 | 71% | 66.4% |
| KnowledgeGemma 4 31B wins | ||
| MMLU | 80% | — |
| GPQA | 79% | 84.3% |
| SuperGPQA | 77% | — |
| MMLU-Pro | 73% | 85.2% |
| HLE | 14% | 26.5% |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| HLE w/o tools | — | 19.5% |
| Instruction Following | ||
| IFEval | 86% | — |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 78% | — |
| Mathematics | ||
| AIME 2023 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | — |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | — |
Gemma 4 31B is ahead overall, 73 to 62. The biggest single separator in this matchup is LiveCodeBench, where the scores are 45% and 80%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 61.1. 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 52.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 73.1 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 58.6. 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.