Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-350M
~24
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 3/8 categoriesGranite-4.0-H-350M· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Granite-4.0-H-350M 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 24. 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 multilingual, where it averages 82.2 against 14.7. The single biggest benchmark swing on the page is MMLU-Pro, 12.1% to 86.7%.
Qwen3.5-122B-A10B is the reasoning model in the pair, while Granite-4.0-H-350M 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 32K for Granite-4.0-H-350M.
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 | Granite-4.0-H-350M | Qwen3.5-122B-A10B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 49.4% |
| BrowseComp | — | 63.8% |
| OSWorld-Verified | — | 58% |
| tau2-bench | — | 79.5% |
| Coding | ||
| HumanEval | 39% | — |
| SWE-bench Verified | — | 72% |
| LiveCodeBench | — | 78.9% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 76.9% |
| Reasoning | ||
| BBH | 33.1% | — |
| LongBench v2 | — | 60.2% |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 35.0% | — |
| GPQA | 24.1% | 86.6% |
| MMLU-Pro | 12.1% | 86.7% |
| SuperGPQA | — | 67.1% |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 55.4% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 14.7% | — |
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-122B-A10B is ahead overall, 71 to 24. The biggest single separator in this matchup is MMLU-Pro, where the scores are 12.1% and 86.7%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 16.4. Inside this category, MMLU-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 55.4. 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 14.7. Granite-4.0-H-350M stays close enough that the answer can still flip depending on your workload.
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