Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
Winner · 2/8 categoriesMistral 8x7B v0.2
28
1/8 categoriesGranite-4.0-H-1B· Mistral 8x7B v0.2
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Mistral 8x7B v0.2 only becomes the better choice if multilingual is the priority.
Granite-4.0-H-1B is clearly ahead on the aggregate, 43 to 28. 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 instruction following, where it averages 77.4 against 67. The single biggest benchmark swing on the page is HumanEval, 74% to 21%. Mistral 8x7B v0.2 does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Granite-4.0-H-1B gives you the larger context window at 128K, compared with 32K for Mistral 8x7B v0.2.
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-1B | Mistral 8x7B v0.2 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 24% |
| BrowseComp | — | 34% |
| OSWorld-Verified | — | 28% |
| Coding | ||
| HumanEval | 74% | 21% |
| SWE-bench Verified | — | 16% |
| LiveCodeBench | — | 12% |
| SWE-bench Pro | — | 14% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 26% |
| OfficeQA Pro | — | 40% |
| Reasoning | ||
| BBH | 60.4% | 62% |
| MuSR | — | 25% |
| LongBench v2 | — | 39% |
| MRCRv2 | — | 38% |
| KnowledgeGranite-4.0-H-1B wins | ||
| MMLU | 59.4% | 29% |
| GPQA | 29.9% | 28% |
| MMLU-Pro | 34.0% | 52% |
| SuperGPQA | — | 26% |
| HLE | — | 3% |
| FrontierScience | — | 35% |
| SimpleQA | — | 27% |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 77.4% | 67% |
| MultilingualMistral 8x7B v0.2 wins | ||
| MGSM | 37.8% | 62% |
| MMLU-ProX | — | 57% |
| Mathematics | ||
| AIME 2023 | — | 29% |
| AIME 2024 | — | 31% |
| AIME 2025 | — | 30% |
| HMMT Feb 2023 | — | 25% |
| HMMT Feb 2024 | — | 27% |
| HMMT Feb 2025 | — | 26% |
| BRUMO 2025 | — | 28% |
| MATH-500 | — | 59% |
Granite-4.0-H-1B is ahead overall, 43 to 28. The biggest single separator in this matchup is HumanEval, where the scores are 74% and 21%.
Granite-4.0-H-1B has the edge for knowledge tasks in this comparison, averaging 32.6 versus 28.4. 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Mistral 8x7B v0.2 has the edge for multilingual tasks in this comparison, averaging 58.8 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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