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

Z.ai released GLM-5.2, its latest large language model, featuring a usable 1-million-token context window and two thinking-effort levels (High and Max), but no benchmark scores at launch. The model, the fourth flagship-tier coding release in four months, targets whole-repository refactors and long-horizon agent runs, with weights to be released under an MIT license next week.

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. 1. Coding agent 2. Context window 3. 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. Check out the Technical details. Also, feel free to follow us on and don’t forget to join our Twitter https://x.com/intent/follow?screen name=marktechpost and Subscribe to 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ . Wait are you on telegram? our Newsletter https://www.aidevsignals.com/ now you can join us on telegram as well. https://t.me/machinelearningresearchnews Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us https://forms.gle/wbash1wF6efRj8G58 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. - Michal Sutter - Michal Sutter - Michal Sutter - Michal Sutter - Michal Sutter