Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeekMath V2
61
3/8 categoriesQwen3.5-122B-A10B
71
Winner · 4/8 categoriesDeepSeekMath V2· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. DeepSeekMath V2 only becomes the better choice if reasoning is the priority.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 61. 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 46.9. The single biggest benchmark swing on the page is LiveCodeBench, 44% to 78.9%. DeepSeekMath V2 does hit back in reasoning, 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 DeepSeekMath V2.
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 | DeepSeekMath V2 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticDeepSeekMath V2 wins | ||
| Terminal-Bench 2.0 | 65% | 49.4% |
| BrowseComp | 66% | 63.8% |
| OSWorld-Verified | 61% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 72% | — |
| SWE-bench Verified | 45% | 72% |
| LiveCodeBench | 44% | 78.9% |
| SWE-bench Pro | 51% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 64% | 76.9% |
| OfficeQA Pro | 73% | — |
| ReasoningDeepSeekMath V2 wins | ||
| MuSR | 75% | — |
| BBH | 86% | — |
| LongBench v2 | 75% | 60.2% |
| MRCRv2 | 72% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 80% | — |
| GPQA | 79% | 86.6% |
| SuperGPQA | 77% | 67.1% |
| MMLU-Pro | 74% | 86.7% |
| HLE | 18% | — |
| FrontierScience | 73% | — |
| SimpleQA | 77% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 83% | 93.4% |
| MultilingualDeepSeekMath V2 wins | ||
| MGSM | 87% | — |
| MMLU-ProX | 80% | 82.2% |
| Mathematics | ||
| AIME 2023 | 80% | — |
| AIME 2024 | 82% | — |
| AIME 2025 | 81% | — |
| HMMT Feb 2023 | 76% | — |
| HMMT Feb 2024 | 78% | — |
| HMMT Feb 2025 | 77% | — |
| BRUMO 2025 | 79% | — |
| MATH-500 | 90% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 61. The biggest single separator in this matchup is LiveCodeBench, where the scores are 44% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 62.3. Inside this category, MMLU-Pro 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 46.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
DeepSeekMath V2 has the edge for reasoning in this comparison, averaging 74 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeekMath V2 has the edge for agentic tasks in this comparison, averaging 63.9 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 68.1. 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 83. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeekMath V2 has the edge for multilingual tasks in this comparison, averaging 82.5 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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