Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-350M
~27
0/8 categoriesGranite-4.0-H-1B
~43
Winner · 3/8 categoriesGranite-4.0-350M· Granite-4.0-H-1B
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Granite-4.0-H-1B is clearly ahead on the aggregate, 43 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Granite-4.0-H-1B's sharpest advantage is in multilingual, where it averages 37.8 against 16.2. The single biggest benchmark swing on the page is HumanEval, 38% to 74%.
Granite-4.0-H-1B gives you the larger context window at 128K, compared with 32K for Granite-4.0-350M.
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-4.0-350M | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | 38% | 74% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| BBH | 33.3% | 60.4% |
| KnowledgeGranite-4.0-H-1B wins | ||
| MMLU | 36.2% | 59.4% |
| GPQA | 26.1% | 29.9% |
| MMLU-Pro | 14.4% | 34.0% |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 61.6% | 77.4% |
| MultilingualGranite-4.0-H-1B wins | ||
| MGSM | 16.2% | 37.8% |
| Mathematics | ||
| Coming soon | ||
Granite-4.0-H-1B is ahead overall, 43 to 27. The biggest single separator in this matchup is HumanEval, where the scores are 38% and 74%.
Granite-4.0-H-1B has the edge for knowledge tasks in this comparison, averaging 32.6 versus 18.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Granite-4.0-H-1B has the edge for instruction following in this comparison, averaging 77.4 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Granite-4.0-H-1B has the edge for multilingual tasks in this comparison, averaging 37.8 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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