GLM-5.2 is the latest large language model from Z.ai, becoming the third major release in the GLM-5 line. It follows GLM-5 (February 11), GLM-5-Turbo (March 15), and GLM-5.1 (April 7). That makes four flagship-tier coding releases in roughly four months.
Usable 1M-Token Context Window
GLM-5.2’s standout spec is a 1,000,000-token context window. Z.ai labels the variant glm-5.2[1m]
in its own configuration. Each response can return up to 131,072 output tokens. That is roughly a 5x jump from GLM-5.1’s 200,000-token window.
A 1M-token window changes how a coding agent works in practice. The agent can hold an entire mid-sized repository in working memory. That includes source files, tests, configuration, and conversation history. It avoids the constant summarization that smaller windows force.
The release also adds two thinking-effort levels: High and Max. Z.ai recommends Max effort for complex, multi-step coding work. In Claude Code, the /effort
command controls this setting. The xhigh, max, and ultracode options all map to GLM-5.2’s Max effort.
Architecture and What Changed
Z.ai did not specify GLM-5.2’s architecture in its launch materials. But based on community notes, the GLM-5 base is a 744-billion-parameter Mixture-of-Experts model. It activates 40 billion parameters per token. GLM-5.1 kept that same backbone with retargeted post-training.
MTP Explainer Playground
Interactive Demo
GLM-5.2 Setup Generator & Context Visualizer
Pick your agent and effort mode. Copy the exact config. See what 1M tokens buys you.
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Coding agent
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Context window
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Thinking effort
Your config
Context window: GLM-5.1 vs GLM-5.2
~200,000 tokens
1,000,000 tokens
GLM-5.2 at a glance
Marktechpost
The Benchmark Question
Here is the important caveat. Z.ai published no benchmark scores for GLM-5.2 at launch. There is no SWE-bench, Terminal-Bench, or Code Arena number yet. The announcement focused on availability, context, and the open-source roadmap.
Specification Comparison: GLM-5.2 vs GLM-5.1
| Attribute | GLM-5.2 | GLM-5.1 |
|---|---|---|
| Released | June 13, 2026 | April 7, 2026 |
| Context window | 1,000,000 tokens (glm-5.2[1m] ) |
~200,000 tokens |
| Max output tokens | 131,072 | Not disclosed |
| Reasoning modes | High, Max | Single mode |
| Architecture | Not specified at launch (GLM-5 lineage) | 744B MoE, 40B active |
| License | MIT (weights pending next week) | MIT (open weights released) |
| Launch benchmarks | None published | 58.4 SWE-bench Pro |
| Access at launch | GLM Coding Plan (all tiers) | Coding Plan, API, and weights |
Use Cases With Examples
Whole-repository refactors: Load a mid-sized repo into one context window. The agent tracks cross-file dependencies without re-fetching. Example: refactor a 40-file Python data pipeline in a single session.Long-horizon agent runs: GLM-5.2 targets sustained plan, execute, test, fix loops. GLM-5.1 sustained roughly 1,700 agent steps in one session. It ran autonomous loops for up to eight hours. GLM-5.2 inherits that trajectory, though its own numbers are pending.Drop-in Claude Code replacement: Swap the base URL and model identifier only. Keep your existing agent harness and workflow. This matters when frontier API access is disrupted.Large-document analysis: Feed long specs, logs, or transcripts past 200K tokens. The 1M window holds material that smaller models truncate.
How to Set Up GLM-5.2
For Claude Code, edit ~/.claude/settings.json
. Point the Sonnet and Opus slots at the 1M variant. Raise the auto-compact window so the agent uses the full context.
{
"env": {
"CLAUDE_CODE_AUTO_COMPACT_WINDOW": "1000000",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-4.5-air",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-5.2[1m]",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-5.2[1m]"
}
}
Alternatively, set the endpoint through environment variables. The Anthropic-compatible endpoint accepts a base-URL swap.
export ANTHROPIC_AUTH_TOKEN="your-zai-api-key"
export ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic"
export ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5.2[1m]"
export ANTHROPIC_DEFAULT_SONNET_MODEL="glm-5.2[1m]"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="glm-4.5-air"
claude
Then run /effort
in a session and select max
. Run /status
to confirm GLM-5.2 is active. For Cline, choose the OpenAI Compatible provider. Set the base URL to https://api.z.ai/api/coding/paas/v4
. Enter the custom model glm-5.2
and set context to 1,000,000.
GLM-5.2 is compatible with eight agentic coding tools from day one. The list includes Claude Code, Cline, OpenCode, and OpenClaw.
Key Takeaways
- Z.ai shipped GLM-5.2 on June 13, 2026, live immediately across all GLM Coding Plan tiers (Lite, Pro, Max, Team).
- 1M-token context window (
glm-5.2[1m]
) with up to 131,072 output tokens. - No benchmarks were published at launch
- It drops into Claude Code, Cline, and OpenClaw via an Anthropic-compatible endpoint with just a base-URL and model swap.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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