Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5
41
Winner · 3/8 categoriesGranite-4.0-H-350M
~24
0/8 categoriesGLM-4.5· Granite-4.0-H-350M
Pick GLM-4.5 if you want the stronger benchmark profile. Granite-4.0-H-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-4.5 is clearly ahead on the aggregate, 41 to 24. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.5's sharpest advantage is in multilingual, where it averages 58.1 against 14.7. The single biggest benchmark swing on the page is MGSM, 60% to 14.7%.
GLM-4.5 gives you the larger context window at 128K, compared with 32K for Granite-4.0-H-350M.
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 | GLM-4.5 | Granite-4.0-H-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 28% | — |
| BrowseComp | 37% | — |
| OSWorld-Verified | 31% | — |
| Coding | ||
| HumanEval | 29% | 39% |
| SWE-bench Verified | 18% | — |
| LiveCodeBench | 13% | — |
| SWE-bench Pro | 15% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 36% | — |
| OfficeQA Pro | 47% | — |
| Reasoning | ||
| MuSR | 33% | — |
| BBH | 61% | 33.1% |
| LongBench v2 | 48% | — |
| MRCRv2 | 52% | — |
| KnowledgeGLM-4.5 wins | ||
| MMLU | 37% | 35.0% |
| GPQA | 36% | 24.1% |
| SuperGPQA | 34% | — |
| MMLU-Pro | 51% | 12.1% |
| HLE | 3% | — |
| FrontierScience | 40% | — |
| SimpleQA | 35% | — |
| Instruction FollowingGLM-4.5 wins | ||
| IFEval | 68% | 55.4% |
| MultilingualGLM-4.5 wins | ||
| MGSM | 60% | 14.7% |
| 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, 41 to 24. The biggest single separator in this matchup is MGSM, where the scores are 60% and 14.7%.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32.1 versus 16.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for instruction following in this comparison, averaging 68 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for multilingual tasks in this comparison, averaging 58.1 versus 14.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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