Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 26B A4B
64
1/8 categoriesQwen3.5 397B
68
Winner · 3/8 categoriesGemma 4 26B A4B· Qwen3.5 397B
Pick Qwen3.5 397B if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if coding is the priority or you need the larger 256K context window.
Qwen3.5 397B is clearly ahead on the aggregate, 68 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in reasoning, where it averages 69.7 against 44.1. The single biggest benchmark swing on the page is LiveCodeBench, 77.1% to 39%. 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.
Gemma 4 26B A4B is the reasoning model in the pair, while Qwen3.5 397B 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. Gemma 4 26B A4B gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B.
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 397B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 52.5% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 62.2% |
| Claw-Eval | — | 48.1% |
| QwenClawBench | — | 51.8% |
| QwenWebBench | — | 1162 |
| TAU3-Bench | — | 68.4% |
| DeepPlanning | — | 37.6% |
| Toolathlon | — | 36.3% |
| MCP Atlas | — | 46.1% |
| MCP-Tasks | — | 74.2% |
| WideResearch | — | 74.0% |
| CodingGemma 4 26B A4B wins | ||
| LiveCodeBench | 77.1% | 39% |
| HumanEval | — | 75% |
| SWE-bench Verified | — | 76.2% |
| LiveCodeBench v6 | — | 83.6% |
| SWE-bench Pro | — | 50.9% |
| SWE Multilingual | — | 69.3% |
| NL2Repo | — | 32.2% |
| Multimodal & GroundedQwen3.5 397B wins | ||
| MMMU-Pro | 73.8% | 79% |
| OfficeQA Pro | — | 68% |
| RealWorldQA | — | 83.9% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.6% |
| We-Math | — | 87.9% |
| DynaMath | — | 86.3% |
| MStar | — | 83.8% |
| SimpleVQA | — | 67.1% |
| ChatCVQA | — | 80.8% |
| AI2D_TEST | — | 93.9% |
| CountBench | — | 97.2% |
| RefCOCO (avg) | — | 92.3% |
| ODINW13 | — | 47.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 65.6% |
| ReasoningQwen3.5 397B wins | ||
| BBH | 64.8% | 82% |
| MRCRv2 | 44.1% | 71% |
| MuSR | — | 78% |
| LongBench v2 | — | 63.2% |
| AI-Needle | — | 68.7% |
| KnowledgeQwen3.5 397B wins | ||
| GPQA | 82.3% | 88.4% |
| MMLU-Pro | 82.6% | 87.8% |
| HLE | 17.2% | 28.7% |
| HLE w/o tools | 8.7% | — |
| MMLU | — | 83% |
| SuperGPQA | — | 70.4% |
| MMLU-Redux | — | 94.9% |
| C-Eval | — | 93% |
| FrontierScience | — | 71% |
| SimpleQA | — | 80% |
| Instruction Following | ||
| IFEval | — | 92.6% |
| IFBench | — | 76.5% |
| Multilingual | ||
| MGSM | — | 82% |
| MMLU-ProX | — | 84.7% |
| NOVA-63 | — | 59.1% |
| INCLUDE | — | 85.6% |
| PolyMath | — | 73.3% |
| VWT2k-lite | — | 78.9% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME 2023 | — | 83% |
| AIME 2024 | — | 85% |
| AIME 2025 | — | 84% |
| AIME26 | — | 93.3% |
| HMMT Feb 2023 | — | 79% |
| HMMT Feb 2024 | — | 81% |
| HMMT Feb 2025 | — | 80% |
| HMMT Feb 2025 | — | 94.8% |
| HMMT Nov 2025 | — | 92.7% |
| HMMT Feb 2026 | — | 87.9% |
| MMAnswerBench | — | 80.9% |
| BRUMO 2025 | — | 82% |
| MATH-500 | — | 81% |
Qwen3.5 397B is ahead overall, 68 to 64. The biggest single separator in this matchup is LiveCodeBench, where the scores are 77.1% and 39%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 68.2 versus 56.1. Inside this category, HLE 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 52.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for reasoning in this comparison, averaging 69.7 versus 44.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 74.1 versus 73.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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