Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 (Reasoning)
82
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGLM-5 (Reasoning)· Granite-4.0-350M
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 82 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in multilingual, where it averages 86.4 against 16.2. The single biggest benchmark swing on the page is MGSM, 89% to 16.2%.
GLM-5 (Reasoning) is the reasoning model in the pair, while Granite-4.0-350M 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. GLM-5 (Reasoning) 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 (Reasoning) | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 81% | — |
| BrowseComp | 80% | — |
| OSWorld-Verified | 74% | — |
| Coding | ||
| HumanEval | 88% | 38% |
| SWE-bench Verified | 62% | — |
| LiveCodeBench | 58% | — |
| SWE-bench Pro | 67% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 74% | — |
| OfficeQA Pro | 84% | — |
| Reasoning | ||
| MuSR | 90% | — |
| BBH | 91% | 33.3% |
| LongBench v2 | 86% | — |
| MRCRv2 | 87% | — |
| KnowledgeGLM-5 (Reasoning) wins | ||
| MMLU | 96% | 36.2% |
| GPQA | 94% | 26.1% |
| SuperGPQA | 92% | — |
| MMLU-Pro | 81% | 14.4% |
| HLE | 29% | — |
| FrontierScience | 83% | — |
| SimpleQA | 92% | — |
| Instruction FollowingGLM-5 (Reasoning) wins | ||
| IFEval | 92% | 61.6% |
| MultilingualGLM-5 (Reasoning) wins | ||
| MGSM | 89% | 16.2% |
| MMLU-ProX | 85% | — |
| Mathematics | ||
| AIME 2023 | 98% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 94% | — |
| HMMT Feb 2024 | 96% | — |
| HMMT Feb 2025 | 95% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 92% | — |
GLM-5 (Reasoning) is ahead overall, 82 to 27. The biggest single separator in this matchup is MGSM, where the scores are 89% and 16.2%.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 73.7 versus 18.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for instruction following in this comparison, averaging 92 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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