Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o3-mini
65
2/8 categoriesQwen3.5-27B
71
Winner · 5/8 categorieso3-mini· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. o3-mini only becomes the better choice if reasoning is the priority.
Qwen3.5-27B is clearly ahead on the aggregate, 71 to 65. 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 54.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 67% to 41.6%. o3-mini does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 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 gives you the larger context window at 262K, compared with 200K for o3-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 | o3-mini | Qwen3.5-27B |
|---|---|---|
| Agentico3-mini wins | ||
| Terminal-Bench 2.0 | 67% | 41.6% |
| BrowseComp | 74% | 61% |
| OSWorld-Verified | 61% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 49.3% | 72.4% |
| SWE-bench Pro | 57% | — |
| LiveCodeBench | — | 80.7% |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 73% | 75% |
| OfficeQA Pro | 76% | — |
| Reasoningo3-mini wins | ||
| LongBench v2 | 82% | 60.6% |
| MRCRv2 | 80% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 86.9% | — |
| GPQA | 77.2% | 85.5% |
| FrontierScience | 66% | — |
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 93.9% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MMLU-ProX | 73% | 82.2% |
| Mathematics | ||
| AIME 2024 | 87.3% | — |
Qwen3.5-27B is ahead overall, 71 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 67% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 70.5. Inside this category, GPQA 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 54.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for reasoning in this comparison, averaging 81.1 versus 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o3-mini has the edge for agentic tasks in this comparison, averaging 66.6 versus 51.6. 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 74.4. 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 93.9. 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 73. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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