Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Pro
87
Winner · 3/8 categoriesGranite-4.0-1B
~40
0/8 categoriesGemini 3.1 Pro· Granite-4.0-1B
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you want the cheaper token bill.
Gemini 3.1 Pro is clearly ahead on the aggregate, 87 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Pro's sharpest advantage is in multilingual, where it averages 94.1 against 27.5. The single biggest benchmark swing on the page is MGSM, 96% to 27.5%.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-1B. That is roughly Infinityx on output cost alone. Gemini 3.1 Pro gives you the larger context window at 1M, compared with 128K for Granite-4.0-1B.
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 Pro | Granite-4.0-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 77% | — |
| BrowseComp | 86% | — |
| OSWorld-Verified | 68% | — |
| Coding | ||
| HumanEval | 91% | 73% |
| SWE-bench Verified | 75% | — |
| LiveCodeBench | 71% | — |
| SWE-bench Pro | 72% | — |
| SWE-Rebench | 62.3% | — |
| React Native Evals | 78.9% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 95% | — |
| OfficeQA Pro | 95% | — |
| Reasoning | ||
| MuSR | 93% | — |
| BBH | 92% | 59.7% |
| LongBench v2 | 93% | — |
| MRCRv2 | 90% | — |
| ARC-AGI-2 | 77.1% | — |
| KnowledgeGemini 3.1 Pro wins | ||
| MMLU | 99% | 59.7% |
| GPQA | 97% | 29.7% |
| SuperGPQA | 95% | — |
| MMLU-Pro | 92% | 32.9% |
| HLE | 40% | — |
| FrontierScience | 88% | — |
| SimpleQA | 95% | — |
| Instruction FollowingGemini 3.1 Pro wins | ||
| IFEval | 95% | 78.5% |
| MultilingualGemini 3.1 Pro wins | ||
| MGSM | 96% | 27.5% |
| MMLU-ProX | 93% | — |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 97% | — |
Gemini 3.1 Pro is ahead overall, 87 to 40. The biggest single separator in this matchup is MGSM, where the scores are 96% and 27.5%.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 80.7 versus 31.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for instruction following in this comparison, averaging 95 versus 78.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multilingual tasks in this comparison, averaging 94.1 versus 27.5. 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.