Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1
35
0/8 categoriesQwen3.5-35B-A3B
68
Winner · 7/8 categoriesDeepSeek V3.1· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. DeepSeek V3.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 68 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in coding, where it averages 72.6 against 14.3. The single biggest benchmark swing on the page is LiveCodeBench, 15% to 74.6%.
Qwen3.5-35B-A3B is the reasoning model in the pair, while DeepSeek V3.1 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 128K for DeepSeek V3.1.
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 | DeepSeek V3.1 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-35B-A3B wins | ||
| Terminal-Bench 2.0 | 29% | 40.5% |
| BrowseComp | 39% | 61% |
| OSWorld-Verified | 33% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 25% | — |
| SWE-bench Verified | 13% | 69.2% |
| LiveCodeBench | 15% | 74.6% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 35% | 75.1% |
| OfficeQA Pro | 45% | — |
| ReasoningQwen3.5-35B-A3B wins | ||
| MuSR | 29% | — |
| BBH | 61% | — |
| MRCRv2 | 48% | — |
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 33% | — |
| GPQA | 32% | 84.2% |
| SuperGPQA | 30% | 63.4% |
| MMLU-Pro | 53% | 85.3% |
| HLE | 2% | — |
| FrontierScience | 37% | — |
| SimpleQA | 31% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 67% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 64% | — |
| MMLU-ProX | 59% | 81% |
| Mathematics | ||
| AIME 2023 | 33% | — |
| AIME 2024 | 35% | — |
| AIME 2025 | 34% | — |
| HMMT Feb 2023 | 29% | — |
| HMMT Feb 2024 | 31% | — |
| HMMT Feb 2025 | 30% | — |
| BRUMO 2025 | 32% | — |
| MATH-500 | 59% | — |
Qwen3.5-35B-A3B is ahead overall, 68 to 35. The biggest single separator in this matchup is LiveCodeBench, where the scores are 15% and 74.6%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 30.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 14.3. 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 39.6. DeepSeek V3.1 stays close enough that the answer can still flip depending on your workload.
Qwen3.5-35B-A3B has the edge for agentic tasks in this comparison, averaging 50.5 versus 32.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 39.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multilingual tasks in this comparison, averaging 81 versus 60.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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