Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 mini
68
2/8 categoriesQwen3.5-27B
71
Winner · 4/8 categoriesGPT-5.4 mini· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. GPT-5.4 mini only becomes the better choice if agentic is the priority or you need the larger 400K context window.
Qwen3.5-27B has the cleaner overall profile here, landing at 71 versus 68. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 54.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 60% to 41.6%. GPT-5.4 mini does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 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. GPT-5.4 mini gives you the larger context window at 400K, compared with 262K for Qwen3.5-27B.
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-5.4 mini | Qwen3.5-27B |
|---|---|---|
| AgenticGPT-5.4 mini wins | ||
| Terminal-Bench 2.0 | 60% | 41.6% |
| OSWorld-Verified | 72.1% | 56.2% |
| MCP Atlas | 57.7% | — |
| Toolathlon | 42.9% | — |
| tau2-bench | 93.4% | 79% |
| BrowseComp | — | 61% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Pro | 54.4% | — |
| SWE-bench Verified | — | 72.4% |
| LiveCodeBench | — | 80.7% |
| Multimodal & GroundedGPT-5.4 mini wins | ||
| MMMU-Pro | 76.6% | 75% |
| MMMU-Pro w/ Python | 78% | — |
| OmniDocBench 1.5 | 0.1263 | — |
| ReasoningQwen3.5-27B wins | ||
| MRCRv2 | 40.7% | — |
| MRCR v2 64K-128K | 47.7% | — |
| MRCR v2 128K-256K | 33.6% | — |
| Graphwalks BFS 128K | 76.3% | — |
| Graphwalks Parents 128K | 71.5% | — |
| LongBench v2 | — | 60.6% |
| KnowledgeQwen3.5-27B wins | ||
| GPQA | 88% | 85.5% |
| HLE | 41.5% | — |
| HLE w/o tools | 28.2% | — |
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 87.4% | 95% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| MATH-500 | 97.4% | — |
Qwen3.5-27B is ahead overall, 71 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 60% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 57.4. 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 54.4. GPT-5.4 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 40.7. GPT-5.4 mini stays close enough that the answer can still flip depending on your workload.
GPT-5.4 mini has the edge for agentic tasks in this comparison, averaging 65.6 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for multimodal and grounded tasks in this comparison, averaging 76.6 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 87.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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