Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
0/8 categoriesQwen2.5-VL-32B
~50
Winner · 1/8 categoriesGranite-4.0-1B· Qwen2.5-VL-32B
Pick Qwen2.5-VL-32B if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you need the larger 128K context window.
Qwen2.5-VL-32B is clearly ahead on the aggregate, 50 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen2.5-VL-32B's sharpest advantage is in knowledge, where it averages 60.8 against 31.7. The single biggest benchmark swing on the page is MMLU-Pro, 32.9% to 68.8%.
Granite-4.0-1B 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 | Granite-4.0-1B | Qwen2.5-VL-32B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | 73% | 91.5% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 49.5% |
| Reasoning | ||
| BBH | 59.7% | — |
| KnowledgeQwen2.5-VL-32B wins | ||
| MMLU | 59.7% | — |
| GPQA | 29.7% | 46% |
| MMLU-Pro | 32.9% | 68.8% |
| Instruction Following | ||
| IFEval | 78.5% | — |
| Multilingual | ||
| MGSM | 27.5% | — |
| Mathematics | ||
| Coming soon | ||
Qwen2.5-VL-32B is ahead overall, 50 to 40. The biggest single separator in this matchup is MMLU-Pro, where the scores are 32.9% and 68.8%.
Qwen2.5-VL-32B has the edge for knowledge tasks in this comparison, averaging 60.8 versus 31.7. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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