Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
1/8 categoriesQwen3.5-35B-A3B
66
Winner · 3/8 categoriesGemma 4 26B A4B· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if coding is the priority.
Qwen3.5-35B-A3B has the cleaner overall profile here, landing at 66 versus 64. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-35B-A3B's sharpest advantage is in knowledge, where it averages 79.3 against 56.1. The single biggest benchmark swing on the page is MMLU-Pro, 82.6% to 85.3%. Gemma 4 26B A4B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-35B-A3B 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 | Qwen3.5-35B-A3B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40.5% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 54.5% |
| Tau2-Telecom | — | 81.2% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | 74.6% |
| SWE-bench Verified | — | 69.2% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 73.8% | 75.1% |
| ReasoningQwen3.5-35B-A3B wins | ||
| BBH | 64.8% | — |
| MRCRv2 | 44.1% | — |
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| GPQA | 82.3% | 84.2% |
| MMLU-Pro | 82.6% | 85.3% |
| HLE | 17.2% | — |
| HLE w/o tools | 8.7% | — |
| SuperGPQA | — | 63.4% |
| Instruction Following | ||
| IFEval | — | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-35B-A3B is ahead overall, 66 to 64. The biggest single separator in this matchup is MMLU-Pro, where the scores are 82.6% and 85.3%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 56.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 26B A4B has the edge for coding in this comparison, averaging 77.1 versus 72.6. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for reasoning in this comparison, averaging 59 versus 44.1. Gemma 4 26B A4B stays close enough that the answer can still flip depending on your workload.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 73.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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