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GLM-5.2
About GLM-5.2
GLM-5.2 is an AI model developed by Zhipu AI, in the language category, released in 2026, made available as an open-weights model with 744B params. GLM-5.2 is Zhipu AI's (Z.ai) open-weight 744B-parameter Mixture-of-Experts model with a 1M-token context window, MIT-lic.
On this page you'll find GLM-5.2's full specifications, including a context window of 1M tokens, 3 benchmark results. Review provider pricing and benchmark scores below, or compare GLM-5.2 head-to-head with other language models.
Run GLM-5.2 locally — what it takes
GLM-5.2 (744B) is too large to run on a single consumer or Apple machine. At Q4 its weights alone are about 417 GB — it needs a multi-GPU / datacenter setup:
- ≈ 2× AMD Instinct MI325X
- ≈ 3× NVIDIA B200
Lower quantization (Q3/Q2) or CPU + system-RAM offload (e.g. ktransformers) reduces the requirement at the cost of speed.
Software support
✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.
Community
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GLM-5.2 benchmark scores▶
| Benchmark | Category | Score | Variant |
|---|---|---|---|
| Terminal-Bench 2.0 | agentic | 81% | |
| SWE-bench Pro | code | 62.1% | |
| Humanity's Last Exam | reasoning | 40.5% |
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Frequently asked questions
What is GLM-5.2? ▶
GLM-5.2 is Zhipu AI's (Z.ai) open-weight 744B-parameter Mixture-of-Experts model with a 1M-token context window, MIT-lic It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.
Who created GLM-5.2? ▶
GLM-5.2 was developed by Zhipu AI and released in 2026.
Is GLM-5.2 open source or proprietary? ▶
GLM-5.2 ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.
What is GLM-5.2's context window? ▶
GLM-5.2 supports a context window of 1M tokens, and has 744B params.
How much does GLM-5.2 cost? ▶
Pricing for GLM-5.2 depends on the provider. See the providers table on this page for the latest API rates.
How does GLM-5.2 perform on benchmarks? ▶
GLM-5.2 is benchmarked across 3 evaluations on GenAIList, including Terminal-Bench 2.0 (81%). See the full benchmark table below and compare it with other models.
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