Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
Winner · 1/8 categoriesKimi K2
53
0/8 categoriesGLM-5V-Turbo· Kimi K2
Pick GLM-5V-Turbo if you want the stronger benchmark profile. Kimi K2 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-5V-Turbo is clearly ahead on the aggregate, 58 to 53. 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 52.1. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 60.2%.
GLM-5V-Turbo gives you the larger context window at 200K, compared with 128K for Kimi K2.
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 |
|---|---|---|
| AgenticGLM-5V-Turbo wins | ||
| BrowseComp | 51.9% | 60.2% |
| OSWorld-Verified | 62.3% | — |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 47.1% |
| Tau2-Telecom | — | 66.1% |
| Coding | ||
| SWE-bench Verified | — | 65.8% |
| LiveCodeBench | — | 53.7% |
| 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% | — |
| Reasoning | ||
| hle | — | 44.9% |
| Knowledge | ||
| MMLU | — | 89.5% |
| GPQA | — | 75.1% |
| SuperGPQA | — | 57.2% |
| MMLU-Pro | — | 81.1% |
| SimpleQA | — | 31% |
| Instruction Following | ||
| IFEval | — | 89.8% |
| Multilingual | ||
| sweMultilingual | — | 61.1% |
| Mathematics | ||
| AIME 2024 | — | 69.6% |
| AIME 2025 | — | 49.5% |
| MATH-500 | — | 97.4% |
| HMMT Feb 2025 | — | 38.8% |
GLM-5V-Turbo is ahead overall, 58 to 53. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 60.2%.
GLM-5V-Turbo has the edge for agentic tasks in this comparison, averaging 58 versus 52.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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