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A head-to-head benchmark comparison of GLM 4.5 across 22 evaluations.
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid rea.
Full GLM 4.5 specs →| Benchmark | GLM 4.5 |
|---|---|
| agentic | |
| BrowseComp | 26.4% |
| BrowseComp-Zh | 37.5 |
| GAIA | 66 |
| TAU-bench Airline | 60.4% |
| TAU-bench Retail | 79.7% |
| Terminal-Bench 2.0 | 37.5% |
| WebWalkerQA | 65.6 |
| xbench-DeepSearch | 70 |
| code | |
| LiveCodeBench | 72.9% |
| SWE-bench Verified | 64.2% |
| SciCode | 41.7% |
| general | |
| IFEval | 86.1% |
| MMLU | 90% |
| MMLU-Pro | 84.6% |
| MultiChallenge | 52.8% |
| long-context | |
| Frames | 78.9 |
| math | |
| AIME 2024 | 91% |
| MATH-500 | 98.2% |
| reasoning | |
| GPQA Diamond | 79.1% |
| Humanity's Last Exam | 21.2% |
| safety | |
| SimpleQA | 26.4% |
| tool-use | |
| BFCL v3 | 77.8% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
GLM 4.5 has the strongest result on LiveCodeBench among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 22 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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