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GLM-5.2

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DevPass (LLM Gateway)glm-5.2-fastglm

Quick Info

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Provider
DevPass (LLM Gateway)
Model key
glm-5.2-fast
Release date
Jun 13, 2026
Last updated
Jun 13, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$2.156
Output token cost
$6.776

Limits

Output tokens
131,072 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

DevPass (LLM Gateway)

CoverageBenchmark

A Baidu Cloud third-party technical analysis examines GLM-5.2's architecture and ecosystem positioning in the 2026 LLM landscape. The article reports the model launched on June 15 with a Mixture-of-Experts sparse architecture totaling 744B parameters (40B activated), expanded context window from 200K to 1M tokens, and According to the piece, GLM-5.2 achieved compatibility with eight major domestic computing platforms by June 17, completing what the article describes as an end-to-end training-to-deployment ecosystem. The analysis highlights long-context processing capabilities relevant to legal document analysis and academic paper in

DevPass (LLM Gateway)

Coverage

NIST's Center for AI Standards and Innovation (CAISI) published an independent assessment of Z.ai's GLM-5.2 (dated July 8, 2026), evaluating the model released June 16, 2026. CAISI concluded GLM-5.2 was probably the most capable open-weight AI model at release, with overall capabilities similar to GPT-5.2 (released Dec On agent security and safeguards, CAISI's report covers robustness against hijacking and jailbreaking attacks, noting mixed results and comparing them against other evaluated PRC open-weight models. The assessment places GLM-5.2 in CAISI's tracked comparison of frontier U.S. and PRC models and uses the model as a refer

DevPass (LLM Gateway)

CoverageBenchmark

Semgrep's security research team ran GLM-5.2 (alongside other open-weight models) against their IDOR benchmark using the same dataset and prompt they use to evaluate frontier coding agents. According to the post, among models given nothing but a prompt, the best open-weight option beat Claude Opus 4.8 on this cyber ben The article positions GLM-5.2 as a credible open-weight alternative for agentic cyber/security tasks relative to top closed frontier coding agents. Semgrep frames this as a meaningful shift for security teams evaluating which models to wire into automated code-review and exploit-finding workflows.

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