Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0
57
1/8 categoriesGemma 4 26B A4B
64
Winner · 2/8 categoriesDeepSeek LLM 2.0· Gemma 4 26B A4B
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. DeepSeek LLM 2.0 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 26B A4B is clearly ahead on the aggregate, 64 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 26B A4B's sharpest advantage is in coding, where it averages 77.1 against 43.3. The single biggest benchmark swing on the page is LiveCodeBench, 39% to 77.1%. DeepSeek LLM 2.0 does hit back in knowledge, 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 LLM 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 26B A4B gives you the larger context window at 256K, compared with 128K for DeepSeek LLM 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 LLM 2.0 | Gemma 4 26B A4B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 57% | — |
| CodingGemma 4 26B A4B wins | ||
| HumanEval | 73% | — |
| SWE-bench Verified | 46% | — |
| LiveCodeBench | 39% | 77.1% |
| SWE-bench Pro | 46% | — |
| Multimodal & GroundedGemma 4 26B A4B wins | ||
| MMMU-Pro | 60% | 73.8% |
| OfficeQA Pro | 70% | — |
| Reasoning | ||
| BBH | 81% | 64.8% |
| MRCRv2 | — | 44.1% |
| KnowledgeDeepSeek LLM 2.0 wins | ||
| MMLU | 79% | — |
| GPQA | 78% | 82.3% |
| SuperGPQA | 76% | — |
| MMLU-Pro | 72% | 82.6% |
| HLE | 12% | 17.2% |
| FrontierScience | 67% | — |
| SimpleQA | 77% | — |
| HLE w/o tools | — | 8.7% |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2023 | 80% | — |
| AIME 2024 | 82% | — |
| AIME 2025 | 81% | — |
| HMMT Feb 2023 | 76% | — |
| HMMT Feb 2024 | 78% | — |
| HMMT Feb 2025 | 77% | — |
| MATH-500 | 83% | — |
Gemma 4 26B A4B is ahead overall, 64 to 57. The biggest single separator in this matchup is LiveCodeBench, where the scores are 39% and 77.1%.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 59.1 versus 56.1. Inside this category, MMLU-Pro 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 43.3. Inside this category, LiveCodeBench 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 64.5. 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.