Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3
51
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 4/8 categoriesDeepSeek V3· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. DeepSeek V3 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 51. 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 39.2. The single biggest benchmark swing on the page is LiveCodeBench, 37.6% to 78.9%.
DeepSeek V3 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 Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B is the reasoning model in the pair, while DeepSeek V3 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 128K for DeepSeek V3.
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 | Qwen3.5-122B-A10B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 49.4% |
| BrowseComp | — | 63.8% |
| OSWorld-Verified | — | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| LiveCodeBench | 37.6% | 78.9% |
| SWE-bench Verified | 42% | 72% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 76.9% |
| ReasoningQwen3.5-122B-A10B wins | ||
| LongBench v2 | 48.7% | 60.2% |
| KnowledgeQwen3.5-122B-A10B wins | ||
| GPQA | 59.1% | 86.6% |
| MMLU-Pro | 75.9% | 86.7% |
| SimpleQA | 24.9% | — |
| SuperGPQA | — | 67.1% |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 86.1% | 93.4% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| AIME 2024 | 39.2% | — |
| MATH-500 | 90.2% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 51. The biggest single separator in this matchup is LiveCodeBench, where the scores are 37.6% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 57.5. 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 39.2. 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 48.7. Inside this category, LongBench v2 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 86.1. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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