Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
2/8 categoriesGLM-4.5
40
Winner · 1/8 categoriesGemma 4 E2B· GLM-4.5
Pick GLM-4.5 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if coding is the priority or you want the stronger reasoning-first profile.
GLM-4.5 finishes one point ahead overall, 40 to 39. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
GLM-4.5's sharpest advantage is in reasoning, where it averages 45.3 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 61%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E2B is the reasoning model in the pair, while GLM-4.5 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.
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 | GLM-4.5 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 28% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | 13% |
| HumanEval | — | 29% |
| SWE-bench Verified | — | 18% |
| SWE-bench Pro | — | 15% |
| Multimodal & Grounded | ||
| MMMU-Pro | 44.2% | — |
| ReasoningGLM-4.5 wins | ||
| BBH | 21.9% | 61% |
| MRCRv2 | 19.1% | 52% |
| MuSR | — | 33% |
| LongBench v2 | — | 48% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 43.4% | 36% |
| MMLU-Pro | 60% | 51% |
| MMLU | — | 37% |
| SuperGPQA | — | 34% |
| HLE | — | 3% |
| FrontierScience | — | 40% |
| SimpleQA | — | 35% |
| Instruction Following | ||
| IFEval | — | 68% |
| Multilingual | ||
| MGSM | — | 60% |
| MMLU-ProX | — | 57% |
| Mathematics | ||
| AIME 2023 | — | 37% |
| AIME 2024 | — | 39% |
| AIME 2025 | — | 38% |
| HMMT Feb 2023 | — | 33% |
| HMMT Feb 2024 | — | 35% |
| HMMT Feb 2025 | — | 34% |
| BRUMO 2025 | — | 36% |
| MATH-500 | — | 57% |
GLM-4.5 is ahead overall, 40 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 61%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 32.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 14.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for reasoning in this comparison, averaging 45.3 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
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