Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E4B
~47
2/8 categoriesQwen2.5-VL-32B
~50
Winner · 0/8 categoriesGemma 4 E4B· Qwen2.5-VL-32B
Pick Qwen2.5-VL-32B if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you need the larger 128K context window.
Qwen2.5-VL-32B has the cleaner overall profile here, landing at 50 versus 47. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemma 4 E4B is the reasoning model in the pair, while Qwen2.5-VL-32B 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 E4B gives you the larger context window at 128K, compared with 32K for Qwen2.5-VL-32B.
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 E4B | Qwen2.5-VL-32B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | 52% | — |
| HumanEval | — | 91.5% |
| Multimodal & GroundedGemma 4 E4B wins | ||
| MMMU-Pro | 52.6% | 49.5% |
| Reasoning | ||
| BBH | 33.1% | — |
| MRCRv2 | 25.4% | — |
| KnowledgeGemma 4 E4B wins | ||
| GPQA | 58.6% | 46% |
| MMLU-Pro | 69.4% | 68.8% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| Coming soon | ||
Qwen2.5-VL-32B is ahead overall, 50 to 47. The biggest single separator in this matchup is GPQA, where the scores are 58.6% and 46%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 60.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for multimodal and grounded tasks in this comparison, averaging 52.6 versus 49.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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