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

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

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

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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