Z.ai Launches GLM-5.2 With 1M-Token Usable Context, Two Levels of Reasoning, and No Benchmarks at Launch

GLM-5.2 is the latest major language model from Z.ai, being 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 code releases in about four months.

Userable 1M-Token Content Window

The dominant specification of GLM-5.2 is the context window of 1,000,000 tokens. Z.ai is a different label glm-5.2[1m] in its configuration. Each response can return up to 131,072 output tokens. That’s about a 5x jump from the GLM-5.1 token window of 200,000.

The 1M token window changes how the encoding agent works in practice. An agent can hold an entire medium-sized cache in working memory. That includes source files, tests, configuration, and chat history. It avoids constant compression that forces smaller windows.

Extraction also adds two levels of reasoning: High and High. Z.ai recommends a lot of effort for the complex, multi-step task of coding. In Claude Code, i /effort command controls this setting. The xhigh, max, and ultracode options all map to GLM-5.2’s Max effort.

Architecture and What Has Changed

Z.ai did not specify the GLM-5.2 architecture in its launch materials. But based on public notes, the basis of GLM-5 is the 744-billion-parameter Mixture-of-Experts model. It unlocks 40 billion parameters per token. GLM-5.1 retained that same core after retargeting training.

MTP Explainer Playground

Benchmark question

Here is an important warning. Z.ai did not publish GLM-5.2 benchmark scores at launch. There is no SWE-bench, Terminal-Bench, or Code Arena number yet. The announcement focuses on availability, context, and the open source road.

Comparison of Specifications: GLM-5.2 vs GLM-5.1

Use Cases with examples

• Universal refactors: Load a medium-sized repo in a single-core window. The agent tracks the dependencies of different files without reloading. Example: regenerate a Python data pipeline with 40 files in one session.
• A long-horizon agent is active: GLM-5.2 directs continuous programming, make, check, fix loops. GLM-5.1 supported approximately 1,700 simultaneous agent steps. It used independent loops for up to eight hours. GLM-5.2 finds that trajectory, although its numbers are still pending.
• Changed draw code for Claude: Change the base URL and model identifier only. Maintain your existing agent and workflow. This is important if border API access is interrupted.
• Analysis of large texts: Feeds long specs, logs, or transcripts past 200K tokens. The 1M window holds objects that are shortened by smaller models.

How to set up GLM-5.2

For Claude’s code, edit ~/.claude/settings.json. Identify the Sonnet and Opus spaces in the 1M variant. Raise the auto-pooling 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 destination using the location variable. Anthropic compatible endpoints accept base-URL exchanges.

export ANTHROPIC_AUTH_TOKEN="your-zai-api-key"
export ANTHROPIC_BASE_URL="
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 the session and choose max. Run /status to confirm that GLM-5.2 is valid. For Cline, choose an OpenAI Compliant provider. Set the base URL to https://api.z.ai/api/coding/paas/v4. Enter a custom model glm-5.2 and set the context to 1,000,000.

GLM-5.2 is compatible with eight coding tools from day one. The list includes Claude Code, Cline, OpenCode, and OpenClaw.

Key Takeaways

• Z.ai shipped with GLM-5.2 on June 13, 2026, live immediately for all GLM Coding Plans (Lite, Pro, Max, Team).
• 1M-token total window (glm-5.2[1m]) with up to 131,072 output tokens.
• No benchmarks were published at the time of launch
• It comes down to Claude Code, Cline, and OpenClaw with an Anthropic-compatible endpoint with base-URL and model exchange.

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