Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-350M
~27
0/8 categoriesLlama 4 Scout
39
Winner · 3/8 categoriesGranite-4.0-350M· Llama 4 Scout
Pick Llama 4 Scout 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.
Llama 4 Scout is clearly ahead on the aggregate, 39 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Llama 4 Scout's sharpest advantage is in multilingual, where it averages 59.8 against 16.2. The single biggest benchmark swing on the page is MGSM, 16.2% to 63%.
Llama 4 Scout gives you the larger context window at 10M, 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 | Llama 4 Scout |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 39% |
| BrowseComp | — | 48% |
| OSWorld-Verified | — | 37% |
| Coding | ||
| HumanEval | 38% | 39% |
| SWE-bench Verified | — | 12% |
| LiveCodeBench | — | 11% |
| SWE-bench Pro | — | 15% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 60% |
| OfficeQA Pro | — | 55% |
| Reasoning | ||
| BBH | 33.3% | 60% |
| MuSR | — | 43% |
| LongBench v2 | — | 64% |
| MRCRv2 | — | 66% |
| KnowledgeLlama 4 Scout wins | ||
| MMLU | 36.2% | 47% |
| GPQA | 26.1% | 46% |
| MMLU-Pro | 14.4% | 51% |
| SuperGPQA | — | 44% |
| HLE | — | 2% |
| FrontierScience | — | 44% |
| SimpleQA | — | 45% |
| Instruction FollowingLlama 4 Scout wins | ||
| IFEval | 61.6% | 68% |
| MultilingualLlama 4 Scout wins | ||
| MGSM | 16.2% | 63% |
| MMLU-ProX | — | 58% |
| Mathematics | ||
| AIME 2023 | — | 47% |
| AIME 2024 | — | 49% |
| AIME 2025 | — | 48% |
| HMMT Feb 2023 | — | 43% |
| HMMT Feb 2024 | — | 45% |
| HMMT Feb 2025 | — | 44% |
| BRUMO 2025 | — | 46% |
| MATH-500 | — | 57% |
Llama 4 Scout is ahead overall, 39 to 27. The biggest single separator in this matchup is MGSM, where the scores are 16.2% and 63%.
Llama 4 Scout has the edge for knowledge tasks in this comparison, averaging 36.3 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for instruction following in this comparison, averaging 68 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for multilingual tasks in this comparison, averaging 59.8 versus 16.2. 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.