GLM-5.2

GLM-5.2 serves as the base model for Z.ai's GLM-5.x family and is best understood through the post-training infrastructure it introduced. According to Z.ai's official blog, GLM-5.2 was the release where the team "built the stack" for scaling long-horizon task training, bringing together three open-source components: IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous training. These tools, paired with accumulated task environments, form the foundation on which all subsequent improvements in the family have been built, and they are explicitly cited as the engineering groundwork that made later gains possible.

The practical significance of GLM-5.2 lies in its role as a stable, open-weights foundation that other teams can build on. Because GLM-5.3 "uses the same base model as GLM-5.2" with all subsequent improvements coming from post-training, GLM-5.2 represents a reliable checkpoint for developers who want the original base behavior without the more aggressive post-training tuning. It is well suited for teams experimenting with long-context workloads, custom RL pipelines on long-horizon tasks, or self-hosted deployments where reproducible open-weight weights matter more than the latest benchmark scores.

DigitalOceanglm-5.2glm

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Quick Info

Provider: DigitalOcean
Model key: glm-5.2
Release date: Jun 13, 2026
Last updated: Jun 13, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost: $1.40
Output token cost: $4.40

Limits

Output tokens: 131,072 tokens
Context window: 262,144 tokens

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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

DigitalOcean

CoverageAnalysis

A technical analysis from Baidu Cloud discusses GLM-5.2 as a next-generation open-source coding model released in June 2026, built on the GLM-5 series architecture with systematic optimizations for complex engineering scenarios, long-context agent tasks, and computational efficiency. The model maintains the series' ult A core architectural innovation described is the DeepSeek Sparse Attention (DSA) mechanism, which resolves the traditional O(n²) attention bottleneck through a 3D importance evaluation module based on token syntax roles, contextual relevance, and task correlation, plus dynamic attention allocation that preserves full a

DigitalOcean

Coverage

The Center for AI Standards and Innovation (CAISI) at NIST published an independent assessment of Z.ai's GLM-5.2, which was released as an open-weight model by the PRC-based company Z.ai (formerly Zhipu AI) on June 16, 2026. According to CAISI's evaluations, GLM-5.2 was probably the most capable open-weight AI model at CAISI's assessment also found mixed safeguards performance: GLM-5.2's safeguards allow assistance with agentic cyber exploit development and block fewer sensitive biological questions than reference U.S. models, but the model appears potentially more robust against agent hijacking and jailbreaking than other evaluated

DigitalOcean

CoverageBenchmark

Databricks published an internal coding-agent benchmark on July 8, 2026, evaluating models and harnesses on real coding tasks performed by engineers on Databricks' multi-million-line codebase, spanning Python, Go, TypeScript, Scala, and other languages. The study concluded that the Pareto frontier for coding tasks (bes Databricks identified three distinct capability tiers and emphasized that token price is a poor indicator of actual end-to-end costs, since larger models can be far more token-efficient. The harness used to call a model dramatically impacted both cost and quality, with simple harnesses like Pi often performing best. Th

DigitalOcean

CoverageBenchmark

Semgrep published a cyber-benchmark evaluation of GLM 5.2, finding that among open-weight models given only a prompt, GLM 5.2 beat Claude Opus 4.8 on the company's IDOR benchmark—the same dataset used to evaluate frontier coding agents. The benchmark specifically targets Insecure Direct Object Reference vulnerabilities This result is significant for developers because it positions GLM 5.2 as a leading open-weight model for unaided cyber-reasoning tasks, outperforming a top closed-source frontier model on a production-relevant vulnerability benchmark. The blog post, authored by Semgrep researchers and dated June 22, 2026, suggests GLM