Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2 (Thinking)
68
1/8 categoriesQwen3.5-122B-A10B
71
Winner · 6/8 categoriesDeepSeek V3.2 (Thinking)· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. DeepSeek V3.2 (Thinking) only becomes the better choice if agentic is the priority.
Qwen3.5-122B-A10B has the cleaner overall profile here, landing at 71 versus 68. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-122B-A10B's sharpest advantage is in coding, where it averages 76.3 against 50.7. The single biggest benchmark swing on the page is LiveCodeBench, 45% to 78.9%. DeepSeek V3.2 (Thinking) does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 128K for DeepSeek V3.2 (Thinking).
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 V3.2 (Thinking) | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticDeepSeek V3.2 (Thinking) wins | ||
| Terminal-Bench 2.0 | 71% | 49.4% |
| BrowseComp | 70% | 63.8% |
| OSWorld-Verified | 67% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 79% | — |
| SWE-bench Verified | 48% | 72% |
| LiveCodeBench | 45% | 78.9% |
| SWE-bench Pro | 58% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 66% | 76.9% |
| OfficeQA Pro | 77% | — |
| ReasoningQwen3.5-122B-A10B wins | ||
| MuSR | 81% | — |
| BBH | 86% | — |
| LongBench v2 | 78% | 60.2% |
| MRCRv2 | 78% | — |
| ARC-AGI-2 | 4% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 87% | — |
| GPQA | 85% | 86.6% |
| SuperGPQA | 83% | 67.1% |
| MMLU-Pro | 73% | 86.7% |
| HLE | 22% | — |
| FrontierScience | 77% | — |
| SimpleQA | 83% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 85% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 84% | — |
| MMLU-ProX | 79% | 82.2% |
| Mathematics | ||
| AIME 2023 | 87% | — |
| AIME 2024 | 89% | — |
| AIME 2025 | 88% | — |
| HMMT Feb 2023 | 83% | — |
| HMMT Feb 2024 | 85% | — |
| HMMT Feb 2025 | 84% | — |
| BRUMO 2025 | 86% | — |
| MATH-500 | 84% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 68. The biggest single separator in this matchup is LiveCodeBench, where the scores are 45% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 65.9. Inside this category, SuperGPQA 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 50.7. 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 60.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for agentic tasks in this comparison, averaging 69.4 versus 56. Inside this category, Terminal-Bench 2.0 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 71. 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 85. 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 80.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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