Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5
75
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGLM-5· Granite-4.0-350M
Pick GLM-5 if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-5 is clearly ahead on the aggregate, 75 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in multilingual, where it averages 82.1 against 16.2. The single biggest benchmark swing on the page is MGSM, 84% to 16.2%.
GLM-5 gives you the larger context window at 200K, compared with 32K for Granite-4.0-350M.
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-5 | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 56.2% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 58% | — |
| Coding | ||
| HumanEval | 80% | 38% |
| SWE-bench Verified | 77.8% | — |
| LiveCodeBench | 52% | — |
| SWE-bench Pro | 46% | — |
| SWE-Rebench | 62.8% | — |
| React Native Evals | 74.2% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 73% | — |
| Reasoning | ||
| MuSR | 82% | — |
| BBH | 83% | 33.3% |
| LongBench v2 | 77% | — |
| MRCRv2 | 73% | — |
| KnowledgeGLM-5 wins | ||
| MMLU | 91.7% | 36.2% |
| GPQA | 86% | 26.1% |
| SuperGPQA | 84% | — |
| MMLU-Pro | 82% | 14.4% |
| HLE | 30.5% | — |
| FrontierScience | 74% | — |
| SimpleQA | 84% | — |
| Instruction FollowingGLM-5 wins | ||
| IFEval | 85% | 61.6% |
| MultilingualGLM-5 wins | ||
| MGSM | 84% | 16.2% |
| MMLU-ProX | 81% | — |
| Mathematics | ||
| AIME 2023 | 88% | — |
| AIME 2024 | 90% | — |
| AIME 2025 | 93.3% | — |
| HMMT Feb 2023 | 84% | — |
| HMMT Feb 2024 | 86% | — |
| HMMT Feb 2025 | 85% | — |
| BRUMO 2025 | 87% | — |
| MATH-500 | 97.4% | — |
GLM-5 is ahead overall, 75 to 27. The biggest single separator in this matchup is MGSM, where the scores are 84% and 16.2%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 69.7 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 85 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multilingual tasks in this comparison, averaging 82.1 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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