Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5-Air
37
0/8 categoriesQwen3.5-35B-A3B
68
Winner · 5/8 categoriesGLM-4.5-Air· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. GLM-4.5-Air only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 68 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in coding, where it averages 72.6 against 22.9. The single biggest benchmark swing on the page is LiveCodeBench, 15% to 74.6%.
Qwen3.5-35B-A3B is the reasoning model in the pair, while GLM-4.5-Air 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 128K for GLM-4.5-Air.
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-Air | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-35B-A3B wins | ||
| Terminal-Bench 2.0 | 28% | 40.5% |
| OSWorld-Verified | 28% | 54.5% |
| BrowseComp | — | 61% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 27% | — |
| SWE-bench Verified | 15% | 69.2% |
| LiveCodeBench | 15% | 74.6% |
| SWE-bench Pro | 14% | — |
| SWE-Rebench | 38.3% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 36% | 75.1% |
| OfficeQA Pro | 44% | — |
| ReasoningQwen3.5-35B-A3B wins | ||
| MuSR | 31% | — |
| BBH | 63% | — |
| LongBench v2 | 47% | 59% |
| MRCRv2 | 51% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 35% | — |
| GPQA | 34% | 84.2% |
| SuperGPQA | 32% | 63.4% |
| MMLU-Pro | 51% | 85.3% |
| HLE | 4% | — |
| FrontierScience | 37% | — |
| SimpleQA | 33% | — |
| Instruction Following | ||
| IFEval | — | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| AIME 2023 | 35% | — |
| AIME 2024 | 37% | — |
| AIME 2025 | 36% | — |
| HMMT Feb 2023 | 31% | — |
| HMMT Feb 2024 | 33% | — |
| HMMT Feb 2025 | 32% | — |
| BRUMO 2025 | 34% | — |
Qwen3.5-35B-A3B is ahead overall, 68 to 37. The biggest single separator in this matchup is LiveCodeBench, where the scores are 15% and 74.6%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 31. Inside this category, GPQA 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 22.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for reasoning in this comparison, averaging 59 versus 44.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for agentic tasks in this comparison, averaging 50.5 versus 28. Inside this category, OSWorld-Verified 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 39.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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