Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 (Reasoning)
82
Winner · 4/8 categoriesQwen3.6 Plus
69
3/8 categoriesGLM-5 (Reasoning)· Qwen3.6 Plus
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if coding is the priority or you need the larger 1M context window.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 82 to 69. 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 reasoning, where it averages 87.4 against 62. The single biggest benchmark swing on the page is DeepPlanning, 14.6% to 41.5%. Qwen3.6 Plus does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus gives you the larger context window at 1M, compared with 200K for GLM-5 (Reasoning).
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) | Qwen3.6 Plus |
|---|---|---|
| AgenticGLM-5 (Reasoning) wins | ||
| Terminal-Bench 2.0 | 81% | 61.6% |
| BrowseComp | 80% | — |
| OSWorld-Verified | 74% | 62.5% |
| DeepPlanning | 14.6% | 41.5% |
| Claw-Eval | — | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| CodingQwen3.6 Plus wins | ||
| HumanEval | 88% | — |
| SWE-bench Verified | 62% | 78.8% |
| LiveCodeBench | 58% | — |
| SWE-bench Pro | 67% | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 74% | 78.8% |
| OfficeQA Pro | 84% | — |
| MMMU | — | 86.0% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| ReasoningGLM-5 (Reasoning) wins | ||
| MuSR | 90% | — |
| BBH | 91% | — |
| LongBench v2 | 86% | 62% |
| MRCRv2 | 87% | — |
| AI-Needle | — | 68.3% |
| KnowledgeGLM-5 (Reasoning) wins | ||
| MMLU | 96% | — |
| GPQA | 94% | 90.4% |
| SuperGPQA | 92% | 71.6% |
| MMLU-Pro | 81% | 88.5% |
| HLE | 29% | 28.8% |
| FrontierScience | 83% | — |
| SimpleQA | 92% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 92% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualGLM-5 (Reasoning) wins | ||
| MGSM | 89% | — |
| MMLU-ProX | 85% | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| 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% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
GLM-5 (Reasoning) is ahead overall, 82 to 69. The biggest single separator in this matchup is DeepPlanning, where the scores are 14.6% and 41.5%.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 73.7 versus 66. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for coding in this comparison, averaging 64.9 versus 62.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for reasoning in this comparison, averaging 87.4 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 78.3 versus 62. Inside this category, DeepPlanning is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 78.8 versus 78.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for instruction following in this comparison, averaging 94.3 versus 92. 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 84.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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