Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Sonnet 4.5
69
4/8 categoriesQwen3.5-122B-A10B
71
Winner · 3/8 categoriesClaude Sonnet 4.5· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B has the cleaner overall profile here, landing at 71 versus 69. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-122B-A10B's sharpest advantage is in coding, where it averages 76.3 against 60.8. The single biggest benchmark swing on the page is LiveCodeBench, 53% to 78.9%. Claude Sonnet 4.5 does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.5 is also the more expensive model on tokens at $3.00 input / $15.00 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. Qwen3.5-122B-A10B is the reasoning model in the pair, while Claude Sonnet 4.5 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 200K for Claude Sonnet 4.5.
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 | Claude Sonnet 4.5 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticClaude Sonnet 4.5 wins | ||
| Terminal-Bench 2.0 | 50% | 49.4% |
| BrowseComp | 74% | 63.8% |
| OSWorld-Verified | 61.4% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 87% | — |
| SWE-bench Verified | 77.2% | 72% |
| LiveCodeBench | 53% | 78.9% |
| SWE-bench Pro | 60% | — |
| SWE-Rebench | 60% | — |
| Multimodal & GroundedClaude Sonnet 4.5 wins | ||
| MMMU-Pro | 95% | 76.9% |
| OfficeQA Pro | 87% | — |
| ReasoningClaude Sonnet 4.5 wins | ||
| MuSR | 89% | — |
| BBH | 88% | — |
| LongBench v2 | 82% | 60.2% |
| MRCRv2 | 81% | — |
| ARC-AGI-2 | 13.6% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 95% | — |
| GPQA | 83.4% | 86.6% |
| SuperGPQA | 91% | 67.1% |
| MMLU-Pro | 84% | 86.7% |
| HLE | 21% | — |
| FrontierScience | 84% | — |
| SimpleQA | 91% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 90% | 93.4% |
| MultilingualClaude Sonnet 4.5 wins | ||
| MGSM | 91% | — |
| MMLU-ProX | 87% | 82.2% |
| Mathematics | ||
| AIME 2023 | 97% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 87% | — |
| HMMT Feb 2023 | 93% | — |
| HMMT Feb 2024 | 95% | — |
| HMMT Feb 2025 | 94% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 88% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 69. The biggest single separator in this matchup is LiveCodeBench, where the scores are 53% and 78.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 71.2. 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 60.8. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for reasoning in this comparison, averaging 66.1 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for agentic tasks in this comparison, averaging 60 versus 56. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 91.4 versus 76.9. 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 90. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for multilingual tasks in this comparison, averaging 88.4 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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