Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E4B
~47
0/8 categoriesGLM-4.7
74
Winner · 4/8 categoriesGemma 4 E4B· GLM-4.7
Pick GLM-4.7 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-4.7 is clearly ahead on the aggregate, 74 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in reasoning, where it averages 78.9 against 25.4. The single biggest benchmark swing on the page is MRCRv2, 25.4% to 78%.
GLM-4.7 gives you the larger context window at 200K, compared with 128K for Gemma 4 E4B.
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 | GLM-4.7 |
|---|---|---|
| Agentic | ||
| OSWorld-Verified | — | 61% |
| VITA-Bench | — | 15.5% |
| CodingGLM-4.7 wins | ||
| LiveCodeBench | 52% | 84.9% |
| HumanEval | — | 94.2% |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Pro | — | 51% |
| Multimodal & GroundedGLM-4.7 wins | ||
| MMMU-Pro | 52.6% | 66% |
| OfficeQA Pro | — | 76% |
| ReasoningGLM-4.7 wins | ||
| BBH | 33.1% | 84% |
| MRCRv2 | 25.4% | 78% |
| MuSR | — | 80% |
| LongBench v2 | — | 79% |
| KnowledgeGLM-4.7 wins | ||
| GPQA | 58.6% | 85.7% |
| MMLU-Pro | 69.4% | 84.3% |
| MMLU | — | 86% |
| SuperGPQA | — | 82% |
| FrontierScience | — | 72% |
| SimpleQA | — | 46% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | — | 94% |
| MMLU-ProX | — | 78% |
| Mathematics | ||
| AIME 2023 | — | 86% |
| AIME 2024 | — | 88% |
| AIME 2025 | — | 95.7% |
| HMMT Feb 2023 | — | 82% |
| HMMT Feb 2025 | — | 97.1% |
| BRUMO 2025 | — | 85% |
GLM-4.7 is ahead overall, 74 to 47. The biggest single separator in this matchup is MRCRv2, where the scores are 25.4% and 78%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 74.8 versus 65.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 69.3 versus 52. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for reasoning in this comparison, averaging 78.9 versus 25.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 52.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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