Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 3 27B
38
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 7/8 categoriesGemma 3 27B· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Gemma 3 27B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in coding, where it averages 76.3 against 16. The single biggest benchmark swing on the page is LiveCodeBench, 15% to 78.9%.
Qwen3.5-122B-A10B is the reasoning model in the pair, while Gemma 3 27B 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. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 32K for Gemma 3 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 | Gemma 3 27B | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticQwen3.5-122B-A10B wins | ||
| Terminal-Bench 2.0 | 29% | 49.4% |
| BrowseComp | 42% | 63.8% |
| OSWorld-Verified | 35% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 37% | — |
| SWE-bench Verified | 16% | 72% |
| LiveCodeBench | 15% | 78.9% |
| SWE-bench Pro | 17% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 39% | 76.9% |
| OfficeQA Pro | 45% | — |
| ReasoningQwen3.5-122B-A10B wins | ||
| MuSR | 41% | — |
| BBH | 62% | — |
| LongBench v2 | 47% | 60.2% |
| MRCRv2 | 44% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 45% | — |
| GPQA | 44% | 86.6% |
| SuperGPQA | 42% | 67.1% |
| MMLU-Pro | 50% | 86.7% |
| HLE | 3% | — |
| FrontierScience | 42% | — |
| SimpleQA | 43% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 67% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 64% | — |
| MMLU-ProX | 60% | 82.2% |
| Mathematics | ||
| AIME 2023 | 45% | — |
| AIME 2024 | 47% | — |
| AIME 2025 | 46% | — |
| HMMT Feb 2023 | 41% | — |
| HMMT Feb 2024 | 43% | — |
| HMMT Feb 2025 | 42% | — |
| BRUMO 2025 | 44% | — |
| MATH-500 | 56% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 38. The biggest single separator in this matchup is LiveCodeBench, where the scores are 15% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 35.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 16. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for reasoning in this comparison, averaging 60.2 versus 44.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56 versus 34.4. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for instruction following in this comparison, averaging 93.4 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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