Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGranite-4.0-1B· Granite-4.0-350M
Pick Granite-4.0-1B 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-4.0-1B is clearly ahead on the aggregate, 40 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Granite-4.0-1B's sharpest advantage is in instruction following, where it averages 78.5 against 61.6. The single biggest benchmark swing on the page is HumanEval, 73% to 38%.
Granite-4.0-1B 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-4.0-1B | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | 73% | 38% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| BBH | 59.7% | 33.3% |
| KnowledgeGranite-4.0-1B wins | ||
| MMLU | 59.7% | 36.2% |
| GPQA | 29.7% | 26.1% |
| MMLU-Pro | 32.9% | 14.4% |
| Instruction FollowingGranite-4.0-1B wins | ||
| IFEval | 78.5% | 61.6% |
| MultilingualGranite-4.0-1B wins | ||
| MGSM | 27.5% | 16.2% |
| Mathematics | ||
| Coming soon | ||
Granite-4.0-1B is ahead overall, 40 to 27. The biggest single separator in this matchup is HumanEval, where the scores are 73% and 38%.
Granite-4.0-1B has the edge for knowledge tasks in this comparison, averaging 31.7 versus 18.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Granite-4.0-1B has the edge for instruction following in this comparison, averaging 78.5 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Granite-4.0-1B has the edge for multilingual tasks in this comparison, averaging 27.5 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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