Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Pro
87
Winner · 1/8 categoriesGLM-5V-Turbo
~58
0/8 categoriesGemini 3.1 Pro· GLM-5V-Turbo
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if you want the cheaper token bill.
Gemini 3.1 Pro is clearly ahead on the aggregate, 87 to 58. 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 agentic, where it averages 76.1 against 58. The single biggest benchmark swing on the page is BrowseComp, 86% to 51.9%.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $1.20 input / $4.00 output per 1M tokens for GLM-5V-Turbo. Gemini 3.1 Pro gives you the larger context window at 1M, compared with 200K for GLM-5V-Turbo.
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 | GLM-5V-Turbo |
|---|---|---|
| AgenticGemini 3.1 Pro wins | ||
| Terminal-Bench 2.0 | 77% | — |
| BrowseComp | 86% | 51.9% |
| OSWorld-Verified | 68% | 62.3% |
| BrowseComp-VL | — | 51.9% |
| OSWorld | — | 62.3% |
| AndroidWorld | — | 75.7% |
| WebVoyager | — | 88.5% |
| Coding | ||
| HumanEval | 91% | — |
| 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% | — |
| Design2Code | — | 94.8% |
| Flame-VLM-Code | — | 93.8% |
| Vision2Web | — | 31.0% |
| ImageMining | — | 30.7% |
| MMSearch | — | 72.9% |
| MMSearch-Plus | — | 30.0% |
| SimpleVQA | — | 78.2% |
| Facts-VLM | — | 58.6% |
| V* | — | 89.0% |
| Reasoning | ||
| MuSR | 93% | — |
| BBH | 92% | — |
| LongBench v2 | 93% | — |
| MRCRv2 | 90% | — |
| ARC-AGI-2 | 77.1% | — |
| Knowledge | ||
| MMLU | 99% | — |
| GPQA | 97% | — |
| SuperGPQA | 95% | — |
| MMLU-Pro | 92% | — |
| HLE | 40% | — |
| FrontierScience | 88% | — |
| SimpleQA | 95% | — |
| Instruction Following | ||
| IFEval | 95% | — |
| Multilingual | ||
| MGSM | 96% | — |
| 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 58. The biggest single separator in this matchup is BrowseComp, where the scores are 86% and 51.9%.
Gemini 3.1 Pro has the edge for agentic tasks in this comparison, averaging 76.1 versus 58. Inside this category, BrowseComp 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.