中文翻译
摘要: GLM-5.2
GLM-5.2 Just Beat GPT-5.5 at a Sixth of the Cost
GLM-5.2 is a 753B-parameter open-weight AI model with a 1M-token context window, MIT licensing, and coding performance that rivals GPT-5.5 an...
正文
GLM-5.2
GLM-5.2 Just Beat GPT-5.5 at a Sixth of the Cost
GLM-5.2 is a 753B-parameter open-weight AI model with a 1M-token context window, MIT licensing, and coding performance that rivals GPT-5.5 and Claude Opus 4.8 while costing significantly less to deploy and operate.
akash rawal
Jun 22, 2026
10 min read
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GLM-5.2
Most AI releases in 2026 follow the same pattern: a closed model from a US lab, a big price tag, and a benchmark table you can't reproduce. GLM-5.2 breaks that pattern.
Released on June 17, 2026, by Beijing-based Z.ai (formerly Zhipu AI), GLM-5.2 is a 753-billion-parameter open-weight model under the MIT license with a 1M-token context window that beats GPT-5.5 on FrontierSWE at roughly one-sixth the cost. (
It ranked first on Design Arena, second on Code Arena Frontend, and topped the open-weight category of the Artificial Analysis Intelligence Index v4.1. For a model whose weights you can freely download and self-host, that is a serious statement.
What Is GLM-5.2?
GLM stands for General Language Model. It is the flagship model series from Z.ai, a Beijing-based AI research company founded in 2019 as a spinout from Tsinghua University's Knowledge Engineering Group.
GLM-5.2
is Z.ai's newest flagship AI model, announced on June 13, 2026, as the third major release in the GLM-5 family for agentic coding. It is built on the same 744-billion-parameter Mixture-of-Experts architecture as GLM-5, with a usable 1-million-token context window and a new dual thinking-effort system (High and Max).
Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5.2 carries that foundation forward and adds three key improvements: a solid 1M context window, IndexShare for efficient sparse attention, and an improved multi-token prediction layer for faster decoding.
Architecture: IndexShare and the 1M Context Problem
Architectur
采集时间: 2026-06-22 20:16:49
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