Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
1/8 categoriesKimi K2.5 (Reasoning)
76
Winner · 0/8 categoriesGLM-5V-Turbo· Kimi K2.5 (Reasoning)
Pick Kimi K2.5 (Reasoning) 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.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning) 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 Kimi K2.5 (Reasoning).
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 | GLM-5V-Turbo | Kimi K2.5 (Reasoning) |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | 60.6% |
| OSWorld-Verified | 62.3% | 63.3% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 50.8% |
| Coding | ||
| HumanEval | — | 99% |
| SWE-bench Verified | — | 76.8% |
| LiveCodeBench | — | 85% |
| SWE-bench Pro | — | 70% |
| SWE-Rebench | — | 57.4% |
| Multimodal & Grounded | ||
| 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% | — |
| MMMU-Pro | — | 78.5% |
| OfficeQA Pro | — | 77% |
| Reasoning | ||
| MuSR | — | 86% |
| BBH | — | 91% |
| LongBench v2 | — | 61% |
| MRCRv2 | — | 81% |
| Knowledge | ||
| MMLU | — | 92% |
| GPQA | — | 87.6% |
| SuperGPQA | — | 88% |
| MMLU-Pro | — | 87.1% |
| HLE | — | 27% |
| FrontierScience | — | 80% |
| SimpleQA | — | 54% |
| Instruction Following | ||
| IFEval | — | 94% |
| Multilingual | ||
| MGSM | — | 96% |
| MMLU-ProX | — | 86% |
| Mathematics | ||
| AIME 2023 | — | 94% |
| AIME 2024 | — | 96% |
| AIME 2025 | — | 96.1% |
| HMMT Feb 2023 | — | 90% |
| HMMT Feb 2024 | — | 92% |
| HMMT Feb 2025 | — | 95.4% |
| BRUMO 2025 | — | 93% |
| MATH-500 | — | 92% |
Kimi K2.5 (Reasoning) is ahead overall, 76 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 60.6%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 57.6. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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