Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Pro
87
Winner · 7/8 categoriesQwen3.6 Plus
69
0/8 categoriesGemini 3.1 Pro· Qwen3.6 Plus
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Gemini 3.1 Pro is clearly ahead on the aggregate, 87 to 69. 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 reasoning, where it averages 88.3 against 62. The single biggest benchmark swing on the page is LongBench v2, 93% to 62%.
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 Qwen3.6 Plus. That is roughly Infinityx on output cost alone. Qwen3.6 Plus is the reasoning model in the pair, while Gemini 3.1 Pro is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
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 | Qwen3.6 Plus |
|---|---|---|
| AgenticGemini 3.1 Pro wins | ||
| Terminal-Bench 2.0 | 77% | 61.6% |
| BrowseComp | 86% | — |
| OSWorld-Verified | 68% | 62.5% |
| Claw-Eval | 50.0% | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| CodingGemini 3.1 Pro wins | ||
| HumanEval | 91% | — |
| SWE-bench Verified | 75% | 78.8% |
| LiveCodeBench | 71% | — |
| SWE-bench Pro | 72% | 56.6% |
| SWE-Rebench | 62.3% | — |
| React Native Evals | 78.9% | — |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedGemini 3.1 Pro wins | ||
| MMMU-Pro | 95% | 78.8% |
| OfficeQA Pro | 95% | — |
| MMMU | — | 86.0% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| ReasoningGemini 3.1 Pro wins | ||
| MuSR | 93% | — |
| BBH | 92% | — |
| LongBench v2 | 93% | 62% |
| MRCRv2 | 90% | — |
| ARC-AGI-2 | 77.1% | — |
| AI-Needle | — | 68.3% |
| KnowledgeGemini 3.1 Pro wins | ||
| MMLU | 99% | — |
| GPQA | 97% | 90.4% |
| SuperGPQA | 95% | 71.6% |
| MMLU-Pro | 92% | 88.5% |
| HLE | 40% | 28.8% |
| FrontierScience | 88% | — |
| SimpleQA | 95% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingGemini 3.1 Pro wins | ||
| IFEval | 95% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualGemini 3.1 Pro wins | ||
| MGSM | 96% | — |
| MMLU-ProX | 93% | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| 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% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Gemini 3.1 Pro is ahead overall, 87 to 69. The biggest single separator in this matchup is LongBench v2, where the scores are 93% and 62%.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 80.7 versus 66. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for coding in this comparison, averaging 68.8 versus 64.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for reasoning in this comparison, averaging 88.3 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for agentic tasks in this comparison, averaging 76.1 versus 62. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 78.8. Inside this category, MMMU-Pro 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 94.3. 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 84.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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