Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Flash-Lite
56
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGemini 3.1 Flash-Lite· Granite-4.0-350M
Pick Gemini 3.1 Flash-Lite if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you want the cheaper token bill.
Gemini 3.1 Flash-Lite is clearly ahead on the aggregate, 56 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Flash-Lite's sharpest advantage is in multilingual, where it averages 69.8 against 16.2. The single biggest benchmark swing on the page is MGSM, 73% to 16.2%.
Gemini 3.1 Flash-Lite is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-350M. That is roughly Infinityx on output cost alone. Gemini 3.1 Flash-Lite gives you the larger context window at 1M, 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 | Gemini 3.1 Flash-Lite | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 47% | — |
| BrowseComp | 60% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 55% | 38% |
| SWE-bench Verified | 22% | — |
| LiveCodeBench | 21% | — |
| SWE-bench Pro | 29% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 74% | — |
| OfficeQA Pro | 72% | — |
| Reasoning | ||
| MuSR | 58% | — |
| BBH | 74% | 33.3% |
| LongBench v2 | 69% | — |
| MRCRv2 | 73% | — |
| KnowledgeGemini 3.1 Flash-Lite wins | ||
| MMLU | 63% | 36.2% |
| GPQA | 62% | 26.1% |
| SuperGPQA | 60% | — |
| MMLU-Pro | 63% | 14.4% |
| HLE | 1% | — |
| FrontierScience | 55% | — |
| SimpleQA | 60% | — |
| Instruction FollowingGemini 3.1 Flash-Lite wins | ||
| IFEval | 79% | 61.6% |
| MultilingualGemini 3.1 Flash-Lite wins | ||
| MGSM | 73% | 16.2% |
| MMLU-ProX | 68% | — |
| Mathematics | ||
| AIME 2023 | 63% | — |
| AIME 2024 | 65% | — |
| AIME 2025 | 64% | — |
| HMMT Feb 2023 | 59% | — |
| HMMT Feb 2024 | 61% | — |
| HMMT Feb 2025 | 60% | — |
| BRUMO 2025 | 62% | — |
| MATH-500 | 71% | — |
Gemini 3.1 Flash-Lite is ahead overall, 56 to 27. The biggest single separator in this matchup is MGSM, where the scores are 73% and 16.2%.
Gemini 3.1 Flash-Lite has the edge for knowledge tasks in this comparison, averaging 46.4 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for instruction following in this comparison, averaging 79 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for multilingual tasks in this comparison, averaging 69.8 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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