Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-350M
~24
0/8 categoriesQwen3.5 397B
64
Winner · 3/8 categoriesGranite-4.0-H-350M· Qwen3.5 397B
Pick Qwen3.5 397B if you want the stronger benchmark profile. Granite-4.0-H-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5 397B is clearly ahead on the aggregate, 64 to 24. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in multilingual, where it averages 78.8 against 14.7. The single biggest benchmark swing on the page is MGSM, 14.7% to 82%.
Qwen3.5 397B gives you the larger context window at 128K, 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 397B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 58% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 52% |
| Coding | ||
| HumanEval | 39% | 75% |
| SWE-bench Verified | — | 42% |
| LiveCodeBench | — | 39% |
| SWE-bench Pro | — | 42% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 56% |
| OfficeQA Pro | — | 68% |
| Reasoning | ||
| BBH | 33.1% | 82% |
| MuSR | — | 78% |
| LongBench v2 | — | 72% |
| MRCRv2 | — | 71% |
| KnowledgeQwen3.5 397B wins | ||
| MMLU | 35.0% | 83% |
| GPQA | 24.1% | 82% |
| MMLU-Pro | 12.1% | 73% |
| SuperGPQA | — | 80% |
| HLE | — | 10% |
| FrontierScience | — | 71% |
| SimpleQA | — | 80% |
| Instruction FollowingQwen3.5 397B wins | ||
| IFEval | 55.4% | 82% |
| MultilingualQwen3.5 397B wins | ||
| MGSM | 14.7% | 82% |
| MMLU-ProX | — | 77% |
| Mathematics | ||
| AIME 2023 | — | 83% |
| AIME 2024 | — | 85% |
| AIME 2025 | — | 84% |
| HMMT Feb 2023 | — | 79% |
| HMMT Feb 2024 | — | 81% |
| HMMT Feb 2025 | — | 80% |
| BRUMO 2025 | — | 82% |
| MATH-500 | — | 81% |
Qwen3.5 397B is ahead overall, 64 to 24. The biggest single separator in this matchup is MGSM, where the scores are 14.7% and 82%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 61 versus 16.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for instruction following in this comparison, averaging 82 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multilingual tasks in this comparison, averaging 78.8 versus 14.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.