Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-1B
~40
Winner · 1/8 categoriesMistral 7B v0.3
~29
0/8 categoriesGranite-4.0-1B· Mistral 7B v0.3
Pick Granite-4.0-1B if you want the stronger benchmark profile. Mistral 7B v0.3 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 29. 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 knowledge, where it averages 31.7 against 31.5. The single biggest benchmark swing on the page is HumanEval, 73% to 30.5%.
Granite-4.0-1B gives you the larger context window at 128K, compared with 32K for Mistral 7B v0.3.
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 | Mistral 7B v0.3 |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | 73% | 30.5% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| BBH | 59.7% | — |
| KnowledgeGranite-4.0-1B wins | ||
| MMLU | 59.7% | 60.1% |
| GPQA | 29.7% | — |
| MMLU-Pro | 32.9% | — |
| FrontierScience | — | 34% |
| SimpleQA | — | 28% |
| Instruction Following | ||
| IFEval | 78.5% | — |
| Multilingual | ||
| MGSM | 27.5% | — |
| Mathematics | ||
| Coming soon | ||
Granite-4.0-1B is ahead overall, 40 to 29. The biggest single separator in this matchup is HumanEval, where the scores are 73% and 30.5%.
Granite-4.0-1B has the edge for knowledge tasks in this comparison, averaging 31.7 versus 31.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
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