Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini
58
0/8 categoriesQwen3.5-27B
70
Winner · 6/8 categoriesGPT-4o mini· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. GPT-4o mini 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 58. 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 knowledge, where it averages 80.6 against 62. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 58% to 41.6%.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B is the reasoning model in the pair, while GPT-4o mini 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-4o mini.
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-4o mini | Qwen3.5-27B |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 58% | 41.6% |
| BrowseComp | 49% | 61% |
| OSWorld-Verified | 44% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 87.2% | — |
| SWE-bench Pro | 65% | — |
| SWE-bench Verified | — | 72.4% |
| LiveCodeBench | — | 80.7% |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 66% | 75% |
| OfficeQA Pro | 53% | — |
| ReasoningQwen3.5-27B wins | ||
| LongBench v2 | 49% | 60.6% |
| MRCRv2 | 50% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 82% | — |
| FrontierScience | 62% | — |
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| GPQA | — | 85.5% |
| Instruction Following | ||
| IFEval | — | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 87% | — |
| MMLU-ProX | 68% | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-27B is ahead overall, 70 to 58. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 58% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 62. GPT-4o mini stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 65. GPT-4o mini stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 49.5. 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 50.9. Inside this category, Terminal-Bench 2.0 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 60.2. Inside this category, MMMU-Pro 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 74.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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