Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (medium)
76
Winner · 6/8 categoriesQwen3.6 Plus
69
1/8 categoriesGPT-5 (medium)· Qwen3.6 Plus
Pick GPT-5 (medium) if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
GPT-5 (medium) is clearly ahead on the aggregate, 76 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (medium)'s sharpest advantage is in reasoning, where it averages 82.1 against 62. The single biggest benchmark swing on the page is LongBench v2, 81% to 62%. Qwen3.6 Plus does hit back in instruction following, 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 128K for GPT-5 (medium).
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 | GPT-5 (medium) | Qwen3.6 Plus |
|---|---|---|
| AgenticGPT-5 (medium) wins | ||
| Terminal-Bench 2.0 | 77% | 61.6% |
| BrowseComp | 78% | — |
| OSWorld-Verified | 72% | 62.5% |
| Claw-Eval | — | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| CodingGPT-5 (medium) wins | ||
| HumanEval | 83% | — |
| SWE-bench Verified | 67% | 78.8% |
| LiveCodeBench | 60% | — |
| SWE-bench Pro | 72% | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedGPT-5 (medium) wins | ||
| MMMU-Pro | 89% | 78.8% |
| OfficeQA Pro | 87% | — |
| 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% |
| ReasoningGPT-5 (medium) wins | ||
| MuSR | 85% | — |
| BBH | 92% | — |
| LongBench v2 | 81% | 62% |
| MRCRv2 | 81% | — |
| AI-Needle | — | 68.3% |
| KnowledgeGPT-5 (medium) wins | ||
| MMLU | 91% | — |
| GPQA | 89% | 90.4% |
| SuperGPQA | 87% | 71.6% |
| MMLU-Pro | 81% | 88.5% |
| HLE | 27% | 28.8% |
| FrontierScience | 82% | — |
| SimpleQA | 87% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 88% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualGPT-5 (medium) wins | ||
| MGSM | 90% | — |
| MMLU-ProX | 87% | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME 2023 | 93% | — |
| AIME 2024 | 95% | — |
| AIME 2025 | 94% | — |
| HMMT Feb 2023 | 89% | — |
| HMMT Feb 2024 | 91% | — |
| HMMT Feb 2025 | 90% | — |
| BRUMO 2025 | 92% | — |
| MATH-500 | 92% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
GPT-5 (medium) is ahead overall, 76 to 69. The biggest single separator in this matchup is LongBench v2, where the scores are 81% and 62%.
GPT-5 (medium) has the edge for knowledge tasks in this comparison, averaging 71.2 versus 66. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for coding in this comparison, averaging 66.2 versus 64.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for reasoning in this comparison, averaging 82.1 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for agentic tasks in this comparison, averaging 75.5 versus 62. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for multimodal and grounded tasks in this comparison, averaging 88.1 versus 78.8. 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 88. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for multilingual tasks in this comparison, averaging 88.1 versus 84.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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