Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano
44
1/8 categoriesQwen3.5-122B-A10B
71
Winner · 6/8 categoriesGPT-4.1 nano· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 44. 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 18. The single biggest benchmark swing on the page is GPQA, 50.3% to 86.6%. GPT-4.1 nano does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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 GPT-4.1 nano 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. GPT-4.1 nano gives you the larger context window at 1M, 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 | GPT-4.1 nano | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticQwen3.5-122B-A10B wins | ||
| Terminal-Bench 2.0 | 43% | 49.4% |
| BrowseComp | 62% | 63.8% |
| OSWorld-Verified | 42% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| SWE-bench Pro | 18% | — |
| SWE-bench Verified | — | 72% |
| LiveCodeBench | — | 78.9% |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 53% | 76.9% |
| OfficeQA Pro | 67% | — |
| ReasoningGPT-4.1 nano wins | ||
| LongBench v2 | 75% | 60.2% |
| MRCRv2 | 73% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 80.1% | — |
| GPQA | 50.3% | 86.6% |
| FrontierScience | 51% | — |
| MMLU-Pro | — | 86.7% |
| SuperGPQA | — | 67.1% |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 83.2% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MMLU-ProX | 59% | 82.2% |
| Mathematics | ||
| AIME 2024 | 9.8% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 44. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 86.6%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 50.7. 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 18. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56 versus 47.4. Inside this category, OSWorld-Verified 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 59.3. 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 83.2. 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 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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