Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
0/8 categoriesQwen3.5-35B-A3B
67
Winner · 3/8 categoriesGranite-4.0-1B· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in multilingual, where it averages 81 against 27.5. The single biggest benchmark swing on the page is GPQA, 29.7% to 84.2%.
Qwen3.5-35B-A3B is the reasoning model in the pair, while Granite-4.0-1B 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 128K for Granite-4.0-1B.
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-4.0-1B | Qwen3.5-35B-A3B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40.5% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 54.5% |
| tau2-bench | — | 81.2% |
| Coding | ||
| HumanEval | 73% | — |
| SWE-bench Verified | — | 69.2% |
| LiveCodeBench | — | 74.6% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 75.1% |
| Reasoning | ||
| BBH | 59.7% | — |
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 59.7% | — |
| GPQA | 29.7% | 84.2% |
| MMLU-Pro | 32.9% | 85.3% |
| SuperGPQA | — | 63.4% |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 78.5% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 27.5% | — |
| MMLU-ProX | — | 81% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-35B-A3B is ahead overall, 67 to 40. The biggest single separator in this matchup is GPQA, where the scores are 29.7% and 84.2%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 31.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 78.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multilingual tasks in this comparison, averaging 81 versus 27.5. Granite-4.0-1B stays close enough that the answer can still flip depending on your workload.
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