Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4 Turbo
47
0/8 categoriesQwen3.5-27B
70
Winner · 7/8 categoriesGPT-4 Turbo· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. GPT-4 Turbo only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 15.4. The single biggest benchmark swing on the page is SWE-bench Verified, 5% to 72.4%.
Qwen3.5-27B is the reasoning model in the pair, while GPT-4 Turbo 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 128K for GPT-4 Turbo.
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-4 Turbo | Qwen3.5-27B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 42% | 41.6% |
| BrowseComp | 54% | 61% |
| OSWorld-Verified | 41% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 52% | — |
| SWE-bench Verified | 5% | 72.4% |
| LiveCodeBench | 23% | 80.7% |
| SWE-bench Pro | 14% | — |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 53% | 75% |
| OfficeQA Pro | 58% | — |
| ReasoningQwen3.5-27B wins | ||
| MuSR | 56% | — |
| BBH | 75% | — |
| LongBench v2 | 62% | 60.6% |
| MRCRv2 | 62% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 60% | — |
| GPQA | 60% | 85.5% |
| SuperGPQA | 58% | 65.6% |
| MMLU-Pro | 51% | 86.1% |
| FrontierScience | 52% | — |
| SimpleQA | 58% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 80% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MMLU-ProX | 65% | 82.2% |
| Mathematics | ||
| AIME 2023 | 60% | — |
| AIME 2024 | 62% | — |
| AIME 2025 | 61% | — |
| HMMT Feb 2023 | 56% | — |
| HMMT Feb 2024 | 58% | — |
| HMMT Feb 2025 | 57% | — |
| BRUMO 2025 | 59% | — |
| MATH-500 | 71% | — |
Qwen3.5-27B is ahead overall, 70 to 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 5% and 72.4%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 54.9. Inside this category, MMLU-Pro 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 15.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 60.4. 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 44.7. Inside this category, OSWorld-Verified 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 55.3. 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 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 65. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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