Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Nemotron 3 Ultra 500B
60
2/8 categoriesQwen3.5-122B-A10B
71
Winner · 5/8 categoriesNemotron 3 Ultra 500B· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Nemotron 3 Ultra 500B only becomes the better choice if reasoning is the priority or you need the larger 10M context window.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 60. 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 43.5. The single biggest benchmark swing on the page is LiveCodeBench, 41% to 78.9%. Nemotron 3 Ultra 500B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Nemotron 3 Ultra 500B gives you the larger context window at 10M, compared with 262K for Qwen3.5-122B-A10B.
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 | Nemotron 3 Ultra 500B | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticNemotron 3 Ultra 500B wins | ||
| Terminal-Bench 2.0 | 63% | 49.4% |
| BrowseComp | 69% | 63.8% |
| OSWorld-Verified | 58% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 66% | — |
| SWE-bench Verified | 42% | 72% |
| LiveCodeBench | 41% | 78.9% |
| SWE-bench Pro | 47% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 61% | 76.9% |
| OfficeQA Pro | 74% | — |
| ReasoningNemotron 3 Ultra 500B wins | ||
| MuSR | 69% | — |
| BBH | 85% | — |
| LongBench v2 | 81% | 60.2% |
| MRCRv2 | 85% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 74% | — |
| GPQA | 73% | 86.6% |
| SuperGPQA | 71% | 67.1% |
| MMLU-Pro | 73% | 86.7% |
| HLE | 15% | — |
| FrontierScience | 67% | — |
| SimpleQA | 71% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 84% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 81% | — |
| MMLU-ProX | 79% | 82.2% |
| Mathematics | ||
| AIME 2023 | 74% | — |
| AIME 2024 | 76% | — |
| AIME 2025 | 75% | — |
| HMMT Feb 2023 | 70% | — |
| HMMT Feb 2024 | 72% | — |
| HMMT Feb 2025 | 71% | — |
| BRUMO 2025 | 73% | — |
| MATH-500 | 84% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 60. The biggest single separator in this matchup is LiveCodeBench, where the scores are 41% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 58.1. 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 43.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Nemotron 3 Ultra 500B has the edge for reasoning in this comparison, averaging 79.1 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Nemotron 3 Ultra 500B has the edge for agentic tasks in this comparison, averaging 62.8 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 66.9. 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 84. 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 79.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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