Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (medium)
76
Winner · 4/8 categoriesQwen3.5-27B
71
3/8 categoriesGPT-5 (medium)· Qwen3.5-27B
Pick GPT-5 (medium) if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if coding is the priority or you need the larger 262K context window.
GPT-5 (medium) is clearly ahead on the aggregate, 76 to 71. 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 agentic, where it averages 75.5 against 51.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77% to 41.6%. Qwen3.5-27B does hit back in coding, 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 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) | Qwen3.5-27B |
|---|---|---|
| AgenticGPT-5 (medium) wins | ||
| Terminal-Bench 2.0 | 77% | 41.6% |
| BrowseComp | 78% | 61% |
| OSWorld-Verified | 72% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 83% | — |
| SWE-bench Verified | 67% | 72.4% |
| LiveCodeBench | 60% | 80.7% |
| SWE-bench Pro | 72% | — |
| Multimodal & GroundedGPT-5 (medium) wins | ||
| MMMU-Pro | 89% | 75% |
| OfficeQA Pro | 87% | — |
| ReasoningGPT-5 (medium) wins | ||
| MuSR | 85% | — |
| BBH | 92% | — |
| LongBench v2 | 81% | 60.6% |
| MRCRv2 | 81% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 91% | — |
| GPQA | 89% | 85.5% |
| SuperGPQA | 87% | 65.6% |
| MMLU-Pro | 81% | 86.1% |
| HLE | 27% | — |
| FrontierScience | 82% | — |
| SimpleQA | 87% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 88% | 95% |
| MultilingualGPT-5 (medium) wins | ||
| MGSM | 90% | — |
| MMLU-ProX | 87% | 82.2% |
| 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% | — |
GPT-5 (medium) is ahead overall, 76 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 71.2. 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 66.2. Inside this category, LiveCodeBench 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 60.6. 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 51.6. 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 75. 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.
GPT-5 (medium) has the edge for multilingual tasks in this comparison, averaging 88.1 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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