Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
2/8 categoriesQwen3.5-35B-A3B
67
Winner · 5/8 categoriesDeepSeek Coder 2.0· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. DeepSeek Coder 2.0 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 62. 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 52.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 73% to 40.5%. DeepSeek Coder 2.0 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-A3B is the reasoning model in the pair, while DeepSeek Coder 2.0 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 Coder 2.0.
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 Coder 2.0 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticDeepSeek Coder 2.0 wins | ||
| Terminal-Bench 2.0 | 73% | 40.5% |
| BrowseComp | 62% | 61% |
| OSWorld-Verified | 65% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 82% | — |
| SWE-bench Verified | 51% | 69.2% |
| LiveCodeBench | 45% | 74.6% |
| SWE-bench Pro | 61% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 50% | 75.1% |
| OfficeQA Pro | 69% | — |
| ReasoningDeepSeek Coder 2.0 wins | ||
| MuSR | 76% | — |
| BBH | 84% | — |
| LongBench v2 | 73% | 59% |
| MRCRv2 | 71% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 80% | — |
| GPQA | 79% | 84.2% |
| SuperGPQA | 77% | 63.4% |
| MMLU-Pro | 73% | 85.3% |
| HLE | 14% | — |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 86% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 83% | — |
| MMLU-ProX | 78% | 81% |
| Mathematics | ||
| AIME 2023 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | — |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | — |
Qwen3.5-35B-A3B is ahead overall, 67 to 62. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 73% and 40.5%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 61.1. Inside this category, SuperGPQA 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 52.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 73.1 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for agentic tasks in this comparison, averaging 67.5 versus 50.5. Inside this category, Terminal-Bench 2.0 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 58.6. 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 86. 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 79.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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