Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1
40
0/8 categoriesGLM-5V-Turbo
~58
Winner · 1/8 categoriesDeepSeek V3.1· GLM-5V-Turbo
Pick GLM-5V-Turbo if you want the stronger benchmark profile. DeepSeek V3.1 only becomes the better choice if you want the cheaper token bill.
GLM-5V-Turbo is clearly ahead on the aggregate, 58 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5V-Turbo's sharpest advantage is in agentic, where it averages 58 against 32.9. The single biggest benchmark swing on the page is OSWorld-Verified, 33% to 62.3%.
GLM-5V-Turbo is also the more expensive model on tokens at $1.20 input / $4.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DeepSeek V3.1. That is roughly Infinityx on output cost alone. GLM-5V-Turbo gives you the larger context window at 200K, compared with 128K for DeepSeek V3.1.
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 | DeepSeek V3.1 | GLM-5V-Turbo |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 39% | 51.9% |
| OSWorld-Verified | 33% | 62.3% |
| BrowseComp-VL | — | 51.9% |
| OSWorld | — | 62.3% |
| AndroidWorld | — | 75.7% |
| WebVoyager | — | 88.5% |
| Coding | ||
| HumanEval | 25% | — |
| SWE-bench Verified | 13% | — |
| LiveCodeBench | 15% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 35% | — |
| OfficeQA Pro | 45% | — |
| 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 | 29% | — |
| BBH | 61% | — |
| MRCRv2 | 48% | — |
| Knowledge | ||
| MMLU | 33% | — |
| GPQA | 32% | — |
| SuperGPQA | 30% | — |
| MMLU-Pro | 53% | — |
| HLE | 2% | — |
| FrontierScience | 37% | — |
| SimpleQA | 31% | — |
| Instruction Following | ||
| IFEval | 67% | — |
| Multilingual | ||
| MGSM | 64% | — |
| MMLU-ProX | 59% | — |
| Mathematics | ||
| AIME 2023 | 33% | — |
| AIME 2024 | 35% | — |
| AIME 2025 | 34% | — |
| HMMT Feb 2023 | 29% | — |
| HMMT Feb 2024 | 31% | — |
| HMMT Feb 2025 | 30% | — |
| BRUMO 2025 | 32% | — |
| MATH-500 | 59% | — |
GLM-5V-Turbo is ahead overall, 58 to 40. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 33% and 62.3%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 32.9. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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