Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Grok 4
68
2/8 categoriesQwen3.5-122B-A10B
71
Winner · 5/8 categoriesGrok 4· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Grok 4 only becomes the better choice if agentic 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 68. 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 knowledge, where it averages 81.6 against 65.3. The single biggest benchmark swing on the page is SuperGPQA, 84% to 67.1%. Grok 4 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-122B-A10B is the reasoning model in the pair, while Grok 4 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 Grok 4.
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 | Grok 4 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticGrok 4 wins | ||
| Terminal-Bench 2.0 | 57% | 49.4% |
| BrowseComp | 63% | 63.8% |
| OSWorld-Verified | 56% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 79% | — |
| SWE-bench Verified | 73% | 72% |
| LiveCodeBench | 79.4% | 78.9% |
| SWE-bench Pro | 48% | — |
| React Native Evals | 70.1% | — |
| Multimodal & GroundedGrok 4 wins | ||
| MMMU-Pro | 80% | 76.9% |
| OfficeQA Pro | 76% | — |
| ReasoningQwen3.5-122B-A10B wins | ||
| MuSR | 81% | — |
| BBH | 83% | — |
| LongBench v2 | 72% | 60.2% |
| MRCRv2 | 71% | — |
| ARC-AGI-2 | 16% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 87% | — |
| GPQA | 86% | 86.6% |
| SuperGPQA | 84% | 67.1% |
| MMLU-Pro | 77% | 86.7% |
| HLE | 16% | — |
| FrontierScience | 75% | — |
| SimpleQA | 83% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 82% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 84% | — |
| MMLU-ProX | 79% | 82.2% |
| Mathematics | ||
| AIME 2023 | 87% | — |
| AIME 2024 | 89% | — |
| AIME 2025 | 88% | — |
| HMMT Feb 2023 | 84% | — |
| HMMT Feb 2024 | 86% | — |
| HMMT Feb 2025 | 85% | — |
| BRUMO 2025 | 87% | — |
| MATH-500 | 83% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 68. The biggest single separator in this matchup is SuperGPQA, where the scores are 84% and 67.1%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 65.3. 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 65.8. Inside this category, SWE-bench Verified 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 59.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Grok 4 has the edge for agentic tasks in this comparison, averaging 58.1 versus 56. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Grok 4 has the edge for multimodal and grounded tasks in this comparison, averaging 78.2 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 82. 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 80.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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