Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2
83
Winner · 5/8 categoriesQwen3.5-122B-A10B
71
2/8 categoriesGPT-5.2· Qwen3.5-122B-A10B
Pick GPT-5.2 if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.2 is clearly ahead on the aggregate, 83 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in reasoning, where it averages 82.4 against 60.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 83% to 49.4%. Qwen3.5-122B-A10B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 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. GPT-5.2 gives you the larger context window at 400K, 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-5.2 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticGPT-5.2 wins | ||
| Terminal-Bench 2.0 | 83% | 49.4% |
| BrowseComp | 65.8% | 63.8% |
| OSWorld-Verified | 47.3% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 91% | — |
| SWE-bench Verified | 80% | 72% |
| LiveCodeBench | 79% | 78.9% |
| SWE-bench Pro | 55.6% | — |
| Multimodal & GroundedGPT-5.2 wins | ||
| MMMU-Pro | 95% | 76.9% |
| OfficeQA Pro | 95% | — |
| ReasoningGPT-5.2 wins | ||
| MuSR | 93% | — |
| BBH | 96% | — |
| LongBench v2 | 91% | 60.2% |
| MRCRv2 | 93% | — |
| ARC-AGI-2 | 52.9% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 99% | — |
| GPQA | 92.4% | 86.6% |
| SuperGPQA | 95% | 67.1% |
| MMLU-Pro | 88% | 86.7% |
| HLE | 42% | — |
| FrontierScience | 91% | — |
| SimpleQA | 95% | — |
| Instruction FollowingGPT-5.2 wins | ||
| IFEval | 94% | 93.4% |
| MultilingualGPT-5.2 wins | ||
| MGSM | 95% | — |
| MMLU-ProX | 91% | 82.2% |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 98% | — |
GPT-5.2 is ahead overall, 83 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 83% and 49.4%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 80.2. Inside this category, SuperGPQA 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 70.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for reasoning in this comparison, averaging 82.4 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 66.2 versus 56. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for instruction following in this comparison, averaging 94 versus 93.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multilingual tasks in this comparison, averaging 92.4 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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