Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0
61
Winner · 0/8 categoriesGLM-5V-Turbo
~58
1/8 categoriesDeepSeek LLM 2.0· GLM-5V-Turbo
Pick DeepSeek LLM 2.0 if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if agentic is the priority or you need the larger 200K context window.
DeepSeek LLM 2.0 has the cleaner overall profile here, landing at 61 versus 58. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
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 LLM 2.0. That is roughly Infinityx on output cost alone. GLM-5V-Turbo gives you the larger context window at 200K, compared with 128K for DeepSeek LLM 2.0.
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 LLM 2.0 | GLM-5V-Turbo |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| Terminal-Bench 2.0 | 57% | — |
| BrowseComp | 62% | 51.9% |
| OSWorld-Verified | 56% | 62.3% |
| BrowseComp-VL | — | 51.9% |
| OSWorld | — | 62.3% |
| AndroidWorld | — | 75.7% |
| WebVoyager | — | 88.5% |
| Coding | ||
| HumanEval | 73% | — |
| SWE-bench Verified | 46% | — |
| LiveCodeBench | 39% | — |
| SWE-bench Pro | 46% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 60% | — |
| OfficeQA Pro | 70% | — |
| 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 | 75% | — |
| BBH | 81% | — |
| LongBench v2 | 70% | — |
| MRCRv2 | 69% | — |
| Knowledge | ||
| MMLU | 79% | — |
| GPQA | 78% | — |
| SuperGPQA | 76% | — |
| MMLU-Pro | 72% | — |
| HLE | 12% | — |
| FrontierScience | 67% | — |
| SimpleQA | 77% | — |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| MGSM | 82% | — |
| MMLU-ProX | 77% | — |
| Mathematics | ||
| AIME 2023 | 80% | — |
| AIME 2024 | 82% | — |
| AIME 2025 | 81% | — |
| HMMT Feb 2023 | 76% | — |
| HMMT Feb 2024 | 78% | — |
| HMMT Feb 2025 | 77% | — |
| BRUMO 2025 | 79% | — |
| MATH-500 | 83% | — |
DeepSeek LLM 2.0 is ahead overall, 61 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 62% and 51.9%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 57.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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