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
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font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Helvetica,Arial,sans-serif!important;
max-width:900px!important; margin:24px auto!important; padding:24px!important;
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border-radius:8px!important; padding:9px 14px!important; font-size:13px!important; font-weight:600!important;
cursor:pointer!important; transition:all .15s ease!important;
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border-radius:10px!important; padding:16px!important; margin:0!important;
font-family:”SFMono-Regular”,Consolas,”Liberation Mono”,Menlo,monospace!important;
font-size:12.5px!important; line-height:1.6!important; overflow-x:auto!important; white-space:pre!important;
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}
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
Claude Code
Claude Code (env vars)
OpenClaw
Cline
2. Context window
1M tokens (glm-5.2[1m])
Standard (glm-5.2)
3. Thinking effort
Max (complex coding)
High
Your config
Copy
Context window: GLM-5.1 vs GLM-5.2
GLM-5.1~200,000 tokens
GLM-5.21,000,000 tokens
GLM-5.2 at a glance
1,000,000input tokens in one context window
131,072max output tokens per response
5xlarger than GLM-5.1’s window
8agentic tools supported day one
Config sourced from Z.ai developer docs · June 2026
© Marktechpost
(function(){
var root = document.getElementById(‘mtp-glm52-demo’);
if(!root) return;
var state = { tool:’claude’, ctx:’1m’, eff:’max’ };
function model(){ return state.ctx===’1m’ ? ‘glm-5.2[1m]’ : ‘glm-5.2′; }
function effortLine(){
return state.eff===’max’
? ‘Run /effort in your session and select max for deeper reasoning.’
: ‘Run /effort in your session and select high for faster turns.’;
}
function configs(){
var m = model();
var compact = state.ctx===’1m’ ? ‘1000000’ : ‘200000’;
if(state.tool===’claude’){
return {
text:'{n “env”: {n “CLAUDE_CODE_AUTO_COMPACT_WINDOW”: “‘+compact+'”,n “ANTHROPIC_DEFAULT_HAIKU_MODEL”: “glm-4.5-air”,n “ANTHROPIC_DEFAULT_SONNET_MODEL”: “‘+m+'”,n “ANTHROPIC_DEFAULT_OPUS_MODEL”: “‘+m+'”n }n}’,
tip:’Edit ~/.claude/settings.json. ‘+effortLine()+’ Then run /status to confirm ‘+m+’ is active.’
};
}
if(state.tool===’env’){
return {
text:’export ANTHROPIC_AUTH_TOKEN=”your-zai-api-key”nexport ANTHROPIC_BASE_URL=”https://api.z.ai/api/anthropic”nexport ANTHROPIC_DEFAULT_OPUS_MODEL=”‘+m+'”nexport ANTHROPIC_DEFAULT_SONNET_MODEL=”‘+m+'”nexport ANTHROPIC_DEFAULT_HAIKU_MODEL=”glm-4.5-air”nclaude’,
tip:’Paste into your shell, then launch claude. The Anthropic-compatible endpoint needs only a base-URL and key swap. ‘+effortLine()
};
}
if(state.tool===’openclaw’){
return {
text:'{n “id”: “‘+m+'”,n “name”: “GLM-5.2”,n “reasoning”: true,n “input”: [“text”],n “contextWindow”: ‘+(state.ctx===’1m’?’1000000′:’200000′)+’,n “maxTokens”: 131072n}’,
tip:’Add this object to models.providers.zai.models in ~/.openclaw/openclaw.json. Point agents.defaults.model.primary at zai/’+m+’, then run: openclaw gateway restart.’
};
}
return {
text:’Provider: OpenAI CompatiblenBase URL: https://api.z.ai/api/coding/paas/v4nAPI Key: your-zai-api-keynModel: ‘+m+’nContext size: ‘+(state.ctx===’1m’?’1000000′:’200000′)+’nSupport Images: off’,
tip:’In Cline, choose the OpenAI Compatible provider and enter these values. Adjust temperature to your task. ‘+effortLine()
};
}
function esc(s){ return s.replace(/&/g,’&’).replace(//g,’>’); }
function highlight(s){
s = esc(s);
s = s.replace(/("[^&]*?"|”[^”]*?”)/g, function(m){ return ”+m+”; });
s = s.replace(/b(export|claude|Provider|Base URL|API Key|Model|Context size|Support Images)b/g, ‘$1’);
return s;
}
function render(){
var c = configs();
root.querySelector(‘#g52-out code’).innerHTML = highlight(c.text);
root.querySelector(‘#g52-tip’).innerHTML = ‘How to apply: ‘+c.tip;
root.querySelector(‘#g52-out’).setAttribute(‘data-raw’, c.text);
}
function wire(groupId, key){
var g = root.querySelector(‘#’+groupId);
g.addEventListener(‘click’, function(e){
var b = e.target.closest(‘.g52-btn’); if(!b) return;
state[key] = b.getAttribute(‘data-v’);
g.querySelectorAll(‘.g52-btn’).forEach(function(x){ x.classList.remove(‘is-on’); });
b.classList.add(‘is-on’);
render();
});
}
wire(‘g52-tool’,’tool’); wire(‘g52-ctx’,’ctx’); wire(‘g52-eff’,’eff’);
root.querySelector(‘#g52-copy’).addEventListener(‘click’, function(){
var raw = root.querySelector(‘#g52-out’).getAttribute(‘data-raw’);
var btn = this;
function done(){ btn.textContent=’Copied’; setTimeout(function(){ btn.textContent=’Copy’; },1400); }
if(navigator.clipboard && navigator.clipboard.writeText){
navigator.clipboard.writeText(raw).then(done, done);
} else {
var ta=document.createElement(‘textarea’); ta.value=raw; document.body.appendChild(ta);
ta.select(); try{document.execCommand(‘copy’);}catch(e){} document.body.removeChild(ta); done();
}
});
render();
})();
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
AttributeGLM-5.2GLM-5.1ReleasedJune 13, 2026April 7, 2026Context window1,000,000 tokens (glm-5.2[1m])~200,000 tokensMax output tokens131,072Not disclosedReasoning modesHigh, MaxSingle modeArchitectureNot specified at launch (GLM-5 lineage)744B MoE, 40B activeLicenseMIT (weights pending next week)MIT (open weights released)Launch benchmarksNone published58.4 SWE-bench ProAccess at launchGLM 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.
Copy CodeCopiedUse a different Browser{
“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.
Copy CodeCopiedUse a different Browserexport 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.
Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere.GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans.https://t.co/aOKcqZD5EJAs our new flagship model, GLM-5.2 delivers…— Z.ai (@Zai_org) June 13, 2026
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The post Z.ai Launches GLM-5.2 With a Usable 1M-Token Context, Two Thinking-Effort Levels, and No Benchmarks at Launch appeared first on MarkTechPost.