Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7
72
Winner · 2/8 categoriesQwen3.5-27B
71
5/8 categoriesGLM-4.7· Qwen3.5-27B
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
GLM-4.7 finishes one point ahead overall, 72 to 71. 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-4.7's sharpest advantage is in reasoning, where it averages 78.9 against 60.6. The single biggest benchmark swing on the page is LongBench v2, 79% to 60.6%. Qwen3.5-27B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 41% | 41.6% |
| BrowseComp | 52% | 61% |
| OSWorld-Verified | 61% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 94.2% | — |
| SWE-bench Verified | 73.8% | 72.4% |
| LiveCodeBench | 84.9% | 80.7% |
| SWE-bench Pro | 51% | — |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 66% | 75% |
| OfficeQA Pro | 76% | — |
| ReasoningGLM-4.7 wins | ||
| MuSR | 80% | — |
| BBH | 84% | — |
| LongBench v2 | 79% | 60.6% |
| MRCRv2 | 78% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 86% | — |
| GPQA | 85.7% | 85.5% |
| SuperGPQA | 82% | 65.6% |
| MMLU-Pro | 84.3% | 86.1% |
| HLE | 24.8% | — |
| FrontierScience | 72% | — |
| SimpleQA | 46% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 88% | 95% |
| MultilingualGLM-4.7 wins | ||
| MGSM | 94% | — |
| MMLU-ProX | 78% | 82.2% |
| 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 71. The biggest single separator in this matchup is LongBench v2, where the scores are 79% and 60.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 63.3. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 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 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 50.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multimodal and grounded tasks in this comparison, averaging 75 versus 70.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 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 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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