Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
0/8 categoriesQwen3.5-27B
70
Winner · 4/8 categoriesGemma 4 26B A4B· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 64. 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 56.1. The single biggest benchmark swing on the page is LiveCodeBench, 77.1% to 80.7%.
Qwen3.5-27B gives you the larger context window at 262K, compared with 256K for Gemma 4 26B A4B.
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 | Gemma 4 26B A4B | Qwen3.5-27B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 41.6% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 56.2% |
| Tau2-Telecom | — | 79% |
| Claw-Eval | — | 20.2% |
| CodingQwen3.5-27B wins | ||
| LiveCodeBench | 77.1% | 80.7% |
| SWE-bench Verified | — | 72.4% |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 73.8% | 75% |
| ReasoningQwen3.5-27B wins | ||
| BBH | 64.8% | — |
| MRCRv2 | 44.1% | — |
| LongBench v2 | — | 60.6% |
| KnowledgeQwen3.5-27B wins | ||
| GPQA | 82.3% | 85.5% |
| MMLU-Pro | 82.6% | 86.1% |
| HLE | 17.2% | — |
| HLE w/o tools | 8.7% | — |
| SuperGPQA | — | 65.6% |
| Instruction Following | ||
| IFEval | — | 95% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-27B is ahead overall, 70 to 64. The biggest single separator in this matchup is LiveCodeBench, where the scores are 77.1% and 80.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 56.1. 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 77.1. Inside this category, LiveCodeBench 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 44.1. Gemma 4 26B A4B stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for multimodal and grounded tasks in this comparison, averaging 75 versus 73.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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