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GLM-5.2

GLM-5.2 is positioned as Z.ai's flagship model for long-horizon work, marking a substantial capability leap over its predecessor GLM-5.1. Its central design priority is making extended context genuinely useful rather than merely available: the model is engineered to sustain quality across long, messy coding-agent trajectories where earlier models often degraded. This focus shapes both its headline capability, a solid one-the cataloged API limit, and its improved internal mechanics aimed at keeping that context reliable under real workload pressure.

Architecturally, GLM-5.2 introduces IndexShare, an approach that reuses a single indexer across every four sparse attention layers and reduces per-token compute at one-the cataloged API limit context lengths, paired with an enhanced multi-token prediction layer that extends speculative-decoding acceptance. The model also offers configurable thinking effort levels, letting developers tune the trade-off between coding performance and latency for different stages of an agent pipeline. Weights are publicly released under an MIT license with no regional restrictions, hosted on HuggingFace under the zai-org organization alongside code on GitHub, making GLM-5.2 a strong fit for teams building long-running coding agents, repository-scale refactoring workflows, and other tasks that benefit from sustained, high-quality reasoning across very large contexts.

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Model Oracle AI
Model key
glm-5.2
Release date
Jun 13, 2026
Last updated
Jun 13, 2026
Input modalities
Output modalities
Capabilities

Limits

Output tokens
131,072 tokens
Context window
1,000,000 tokens

Latest news about GLM-5.2

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CoverageAnalysis

A Baidu Cloud technical commentary dated September 7, 2026 describes GLM-5.2 as a next-generation open-source coding model released as an iterative upgrade of the GLM-5 series, explicitly named GLM-5.2 throughout. The article claims GLM-5.2 maintains an ultra-large parameter design of 744 billion total parameters with The piece frames GLM-5.2 as targeting complex engineering scenarios, long-context agent tasks, and computational efficiency, claiming technical benchmarks comparable to top-tier industry reasoning models. These specific architectural claims (744B/40B parameters, DSA mechanism, 3.2× speedup) are presented without cited

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Coverage

The U.S. NIST Center for AI Standards and Innovation (CAISI) published an independent public assessment of Z.ai's GLM-5.2 on July 17, 2026 (report dated July 8, 2026), evaluating the model across overall capabilities, cyber capabilities, agent security, and safeguards. CAISI concluded that GLM-5.2 was "probably the mos On safeguards, CAISI found GLM-5.2's protections allow assistance with agentic cyber exploit development and block fewer sensitive biological questions than reference U.S. models, though it appeared more robust than other evaluated PRC open-weight models against agent hijacking and prompt-based jailbreaks. CAISI noted

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Coverage

Simon Willison reports that Chinese AI lab Z.ai released GLM-5.2 to its coding plan subscribers on June 13, 2026, followed by a full open-weights release under an MIT license on June 16, 2026. The model is a 753B-parameter Mixture-of-Experts architecture with 40B active parameters, weighing in at 1.51TB, and is text-in On independent benchmarks, Artificial Analysis ranked GLM-5.2 as the new leading open-weights model on the Intelligence Index v4.1 with a score of 51, ahead of MiniMax-M3 (44), DeepSeek V4 Pro max (44), and Kimi K2.6 (43), though it is notably token-hungry at 43k output tokens per task versus 24–37k for peers. GLM-5.2

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CoverageBenchmark

Semgrep published a security benchmark comparison in which GLM-5.2 was evaluated against its IDOR benchmark using the same dataset and prompt previously applied to frontier coding agents, as reported on June 22, 2026. The blog, authored by Semgrep researchers including Katie Paxton-Fear, describes the test as pitting o The piece highlights GLM-5.2 as a notable open-weight contender for application-security and code-analysis tasks, suggesting practical relevance for security teams evaluating alternatives to proprietary frontier coding agents. However, the supplied excerpt is truncated before the full quantitative results are shown, so

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