Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1
45
0/8 categoriesGLM-5V-Turbo
~58
Winner · 1/8 categoriesDeepSeek-R1· GLM-5V-Turbo
Pick GLM-5V-Turbo if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GLM-5V-Turbo is clearly ahead on the aggregate, 58 to 45. 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 44.5. The single biggest benchmark swing on the page is OSWorld-Verified, 44% 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 $0.55 input / $2.19 output per 1M tokens for DeepSeek-R1. DeepSeek-R1 is the reasoning model in the pair, while GLM-5V-Turbo 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. GLM-5V-Turbo gives you the larger context window at 200K, compared with 128K for DeepSeek-R1.
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-R1 | GLM-5V-Turbo |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| Terminal-Bench 2.0 | 42% | — |
| BrowseComp | 49% | 51.9% |
| OSWorld-Verified | 44% | 62.3% |
| BrowseComp-VL | — | 51.9% |
| OSWorld | — | 62.3% |
| AndroidWorld | — | 75.7% |
| WebVoyager | — | 88.5% |
| Coding | ||
| HumanEval | 92% | — |
| SWE-bench Verified | 49.2% | — |
| LiveCodeBench | 19% | — |
| SWE-bench Pro | 25% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 43% | — |
| OfficeQA Pro | 53% | — |
| 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 | 40% | — |
| BBH | 66% | — |
| LongBench v2 | 58% | — |
| MRCRv2 | 57% | — |
| ARC-AGI-2 | 1.3% | — |
| Knowledge | ||
| MMLU | 90.8% | — |
| GPQA | 71.5% | — |
| SuperGPQA | 41% | — |
| MMLU-Pro | 84% | — |
| HLE | 14% | — |
| FrontierScience | 44% | — |
| SimpleQA | 30.1% | — |
| Instruction Following | ||
| IFEval | 83.3% | — |
| Multilingual | ||
| MGSM | 61% | — |
| MMLU-ProX | 60% | — |
| Mathematics | ||
| AIME 2023 | 44% | — |
| AIME 2024 | 79.8% | — |
| AIME 2025 | 45% | — |
| HMMT Feb 2023 | 40% | — |
| HMMT Feb 2024 | 42% | — |
| HMMT Feb 2025 | 41% | — |
| BRUMO 2025 | 43% | — |
| MATH-500 | 97.3% | — |
GLM-5V-Turbo is ahead overall, 58 to 45. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 44% and 62.3%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 44.5. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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