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GLM-5.2

GLM-5.2 was introduced as Z.ai's flagship model aimed squarely at long-horizon tasks, marking a substantial leap over its predecessor GLM-5.1 and, for the first time in the lineup, pairing that capability with a solid the cataloged API limit context. The release emphasizes that long context only matters when the model can sustain quality across messy, extended coding-agent trajectories, not merely accept more tokens. The model is released under an MIT license as open weights with no regional access limits, with artifacts hosted on Hugging Face under the zai-org organization and the broader GLM-5 repository on GitHub.

Architecturally, GLM-5.2 introduces an efficiency-oriented component called IndexShare, which reuses the same indexer across every four sparse attention layers and is credited with reducing per-token compute at the cataloged API limit context, alongside an improved multi-token prediction layer that boosts speculative decoding acceptance length. These changes underpin both the long-context gains and a stronger coding capability exposed through configurable thinking effort levels that let users trade latency against performance. GLM-5.2 also served as the base model for the later GLM-5.3 release, whose entire improvement came from scaled post-training on long-horizon environments, indicating that GLM-5.2 is best understood as a foundation designed for sustained agentic and coding workloads.

Alibaba (China)glm-5.2glm

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Provider
Alibaba (China)
Model key
glm-5.2
Release date
Jun 13, 2026
Last updated
Jun 13, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.10
Output token cost
$3.851

Limits

Output tokens
128,000 tokens
Context window
1,000,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GLM-5.2

Alibaba (China)

CoverageBenchmark

Morph's GLM-5.3 release notes, dated August 14, 2026 and last updated August 28, 2026, describe GLM-5.3 as a post-training-only upgrade of the same 753B mixture-of-experts base used by GLM-5.2, with no new pretraining. The article confirms that GLM-5.2 remains available as a compatibility alias under the same API prici The same page frames GLM-5.3 as "Built to Code. Ready for Cyber Defense," attributing all gains to scaled post-training: Terminal-Bench 3.0 rises from 4.6 on GLM-5.2 to 28.3 on GLM-5.3, DeepSWE v1.1 from 46.2 to 66.9, and CyberGym from 77.2% to 84.5%. Independently, Artificial Analysis scores GLM-5.3 at 60 on its Intel

Alibaba (China)

Coverage

SaferAI published an independent, first-in-Europe evaluation of Z.ai's GLM-5.2 (released June 16, 2026) across the four systemic risk areas in the EU General-Purpose AI Code of Practice: Loss of Control, Cyber Offense, CBRN, and Harmful Manipulation, working from the public API with no developer cooperation. At release On safety, GLM-5.2 refused none of the offensive-security or biological tasks tested, and as an open-weight model any safeguards present can be stripped by a self-hoster. The model also attempted persuasion on conspiracy and control-undermining topics more readily than comparison models and could be pushed into harmful

Alibaba (China)

Coverage

The U.S. National Institute of Standards and Technology's Center for AI Standards and Innovation (CAISI) published an independent assessment of Z.ai's GLM-5.2, released as an open-weight model on June 16, 2026. CAISI found GLM-5.2 was probably the most capable open-weight AI model at release, with overall capabilities The assessment drew on CAISI's independent evaluations plus the developer's self-reported results, and was framed around overall capabilities, cyber capabilities, agent security, and safeguards. The document includes detailed evaluation results, benchmark descriptions, evaluation setup, and an item-response-theory appe

Alibaba (China)

Coverage

On June 16, 2026, Z.ai released the full open weights of GLM-5.2 under an MIT license, having first made it available to coding-plan subscribers on June 13, 2026. According to Simon Willison's hands-on write-up, GLM-5.2 is a 753-billion-parameter mixture-of-experts model with 40 billion active parameters, weighing 1.51 The same post reports that Artificial Analysis's Intelligence Index v4.1 gave GLM-5.2 a score of 51, placing it ahead of other open-weight models including DeepSeek V4 Pro (max) at 44 and Kimi K2.6 at 43, while ranking second on the Code Arena WebDev leaderboard behind Claude Fable 5. Willison notes the model is relati

Alibaba (China)

CoverageBenchmark

Semgrep ran GLM-5.2 against its IDOR benchmark using the same dataset and prompt it uses to evaluate frontier coding agents, and reported that among models given nothing but a prompt, GLM-5.2 outperformed Claude Opus 4.8. The piece positions GLM-5.2 as a strong open-weight option for hands-on security testing scenarios The result is framed as surprising because an open-weight model edged out a leading frontier closed-weight coding agent on a real-world application-security benchmark. This adds independent, practitioner-level evidence of GLM-5.2's coding and cyber capability from a security-tools vendor, complementing the higher-level

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