Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
0/8 categoriesK-Exaone
~50
Winner · 1/8 categoriesGemma 4 E2B· K-Exaone
Pick K-Exaone if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
K-Exaone is clearly ahead on the aggregate, 50 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
K-Exaone's sharpest advantage is in coding, where it averages 49.4 against 44.
K-Exaone gives you the larger context window at 256K, compared with 128K for Gemma 4 E2B.
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 E2B | K-Exaone |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| CodingK-Exaone wins | ||
| LiveCodeBench | 44% | — |
| SWE-bench Verified | — | 49.4% |
| Multimodal & Grounded | ||
| MMMU-Pro | 44.2% | — |
| Reasoning | ||
| BBH | 21.9% | — |
| MRCRv2 | 19.1% | — |
| Knowledge | ||
| GPQA | 43.4% | — |
| MMLU-Pro | 60% | — |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| Coming soon | ||
K-Exaone is ahead overall, 50 to 39.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 44. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
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