Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7
72
Winner · 3/8 categoriesQwen3.5-35B-A3B
67
4/8 categoriesGLM-4.7· Qwen3.5-35B-A3B
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
GLM-4.7 is clearly ahead on the aggregate, 72 to 67. 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 59. The single biggest benchmark swing on the page is LongBench v2, 79% to 59%. Qwen3.5-35B-A3B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 200K for GLM-4.7.
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-4.7 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticGLM-4.7 wins | ||
| Terminal-Bench 2.0 | 41% | 40.5% |
| BrowseComp | 52% | 61% |
| OSWorld-Verified | 61% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 94.2% | — |
| SWE-bench Verified | 73.8% | 69.2% |
| LiveCodeBench | 84.9% | 74.6% |
| SWE-bench Pro | 51% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 66% | 75.1% |
| OfficeQA Pro | 76% | — |
| ReasoningGLM-4.7 wins | ||
| MuSR | 80% | — |
| BBH | 84% | — |
| LongBench v2 | 79% | 59% |
| MRCRv2 | 78% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 86% | — |
| GPQA | 85.7% | 84.2% |
| SuperGPQA | 82% | 63.4% |
| MMLU-Pro | 84.3% | 85.3% |
| HLE | 24.8% | — |
| FrontierScience | 72% | — |
| SimpleQA | 46% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 88% | 91.9% |
| MultilingualGLM-4.7 wins | ||
| MGSM | 94% | — |
| MMLU-ProX | 78% | 81% |
| Mathematics | ||
| AIME 2023 | 86% | — |
| AIME 2024 | 88% | — |
| AIME 2025 | 95.7% | — |
| HMMT Feb 2023 | 82% | — |
| HMMT Feb 2024 | 84% | — |
| HMMT Feb 2025 | 97.1% | — |
| BRUMO 2025 | 85% | — |
| MATH-500 | 85% | — |
GLM-4.7 is ahead overall, 72 to 67. The biggest single separator in this matchup is LongBench v2, where the scores are 79% and 59%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 63.3. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 69.3. 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 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 50.8 versus 50.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 70.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 88. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multilingual tasks in this comparison, averaging 83.6 versus 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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