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Model details

GLM-5.2

GLM-5.2 is positioned as a flagship model purpose-built for long-horizon work, marking a substantial leap over its predecessor GLM-5.1 in sustaining quality across extended coding-agent trajectories and reasoning chains. Rather than simply accepting more tokens, the model is engineered to maintain coherence over long, messy agentic sessions, delivering a solid the cataloged API limit context that stays stable during complex multi-step tasks. Its intended use centers on advanced software engineering, agentic workflows, complex reasoning, and large-scale data processing, making it well suited for teams running extended coding agents, deep research pipelines, and production pipelines that exceed typical context windows.

On the architecture side, GLM-5.2 introduces IndexShare, a technique that reuses the same indexer across every four sparse attention layers, reportedly reducing per-token FLOPs by roughly 2.9× at the the cataloged API limit context length and making long-context inference more practical. The model also improves its multi-token prediction layer for speculative decoding, increasing acceptance length by up to 20%, which helps balance performance and latency during generation. Coding capability is strengthened through flexible thinking effort levels, letting users trade latency against depth of reasoning. Released under an MIT open-source license with no regional restrictions, GLM-5.2 is available through Z.ai, the Z.ai Coding Plan, and on Hugging Face, giving practitioners broad access to its long-horizon capabilities.

Volcengine Arkglm-5-2-260617glm

Quick Info

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Provider
Volcengine Ark
Model key
glm-5-2-260617
Release date
Jun 13, 2026
Last updated
Jun 13, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.18747
Output token cost
$4.15615

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

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Coverage

The U.S. NIST Center for AI Standards and Innovation (CAISI) released a public technical assessment of Z.ai's GLM-5.2 on July 17, 2026, evaluating the model that Z.ai had open-weighted on June 16, 2026. CAISI concluded that GLM-5.2 was probably the most capable open-weight model at the time of its release, with overall CAISI's safeguards findings for GLM-5.2 were mixed: the model's safeguards allow assistance with agentic cyber exploit development and block fewer sensitive biological questions than reference U.S. models, but GLM-5.2 appears more robust against agent hijacking and prompt-based jailbreaking attacks than other evaluated

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Coverage

According to a July 8, 2026 assessment from the Center for AI Standards and Innovation (CAISI) at the National Institute of Standards and Technology, Z.ai released GLM-5.2 as an open-weight model on June 16, 2026. CAISI evaluated the model and concluded it was probably the most capable open-weight AI model at release, The same assessment reported that GLM-5.2's safeguards performance was mixed: it allowed assistance with agentic cyber exploit development, blocked fewer sensitive biological questions than reference U.S. models, but appeared potentially more robust against agent hijacking and prompt-based jailbreaking than other evalu

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Coverage

Z.ai released GLM-5.2 to coding plan subscribers on June 13, 2026, then published the full open weights under an MIT license on June 16, 2026. The model is a 753-billion-parameter mixture-of-experts network with 40 billion active parameters per forward pass, roughly 1.51TB in size, and is text-input only (vision handle On the Artificial Analysis Intelligence Index v4.1, GLM-5.2 scored 51, leading open-weights models including MiniMax-M3 (44), DeepSeek V4 Pro max (44), and Kimi K2.6 (43), and it ranked 2nd on the Code Arena WebDev leaderboard behind Claude Fable 5. Pricing across nine OpenRouter providers converges at $1.40 per millio

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CoverageBenchmark

In a June 22, 2026 blog post, Semgrep reported running a set of popular open-source models against its IDOR benchmark using the same dataset and prompt it uses to evaluate frontier coding agents. According to Semgrep, among models given nothing but a prompt, the best open-weight option beat Claude Opus 4.8 in its cyber Semgrep framed the result as a surprise, using the headline "We have Mythos at Home" to position GLM-5.2 as a strong open-weight performer in cyber/security testing scenarios. The post presents GLM-5.2 as a competitive open-source model for cybersecurity-oriented code analysis workloads, though the excerpt focuses on t

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