Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-OSS 20B
35
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 6/8 categoriesGPT-OSS 20B· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. GPT-OSS 20B 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 35. 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 14.4. The single biggest benchmark swing on the page is LiveCodeBench, 11% to 78.9%.
Qwen3.5-122B-A10B is the reasoning model in the pair, while GPT-OSS 20B 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 GPT-OSS 20B.
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-OSS 20B | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticQwen3.5-122B-A10B wins | ||
| Terminal-Bench 2.0 | 35% | 49.4% |
| OSWorld-Verified | 31% | 58% |
| BrowseComp | — | 63.8% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 23% | — |
| SWE-bench Verified | 14% | 72% |
| LiveCodeBench | 11% | 78.9% |
| SWE-bench Pro | 18% | — |
| React Native Evals | 64.3% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 31% | 76.9% |
| OfficeQA Pro | 42% | — |
| ReasoningQwen3.5-122B-A10B wins | ||
| MuSR | 27% | — |
| LongBench v2 | 48% | 60.2% |
| MRCRv2 | 48% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 85.3% | — |
| GPQA | 30% | 86.6% |
| SuperGPQA | 28% | 67.1% |
| MMLU-Pro | 53% | 86.7% |
| HLE | 1% | — |
| FrontierScience | 34% | — |
| SimpleQA | 29% | — |
| Instruction Following | ||
| IFEval | — | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 61% | — |
| MMLU-ProX | 59% | 82.2% |
| Mathematics | ||
| AIME 2023 | 31% | — |
| AIME 2024 | 33% | — |
| AIME 2025 | 32% | — |
| HMMT Feb 2023 | 27% | — |
| HMMT Feb 2024 | 29% | — |
| HMMT Feb 2025 | 28% | — |
| BRUMO 2025 | 30% | — |
| MATH-500 | 59% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 35. The biggest single separator in this matchup is LiveCodeBench, where the scores are 11% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 28.7. 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 14.4. 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 42.4. 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 33.1. 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 36. Inside this category, MMMU-Pro 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 59.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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