Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Exaone 4.0 32B
~76
Winner · 1/8 categoriesGemma 4 26B A4B
64
0/8 categoriesExaone 4.0 32B· Gemma 4 26B A4B
Pick Exaone 4.0 32B if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if you need the larger 256K context window.
Exaone 4.0 32B is clearly ahead on the aggregate, 76 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Exaone 4.0 32B's sharpest advantage is in knowledge, where it averages 81.8 against 56.1. The single biggest benchmark swing on the page is MMLU-Pro, 81.8% to 82.6%.
Gemma 4 26B A4B gives you the larger context window at 256K, compared with 128K for Exaone 4.0 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 | Exaone 4.0 32B | Gemma 4 26B A4B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 77.1% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 73.8% |
| Reasoning | ||
| BBH | — | 64.8% |
| MRCRv2 | — | 44.1% |
| KnowledgeExaone 4.0 32B wins | ||
| MMLU-Pro | 81.8% | 82.6% |
| GPQA | — | 82.3% |
| HLE | — | 17.2% |
| HLE w/o tools | — | 8.7% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2025 | 85.3% | — |
Exaone 4.0 32B is ahead overall, 76 to 64. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 82.6%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 56.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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