GLM-5.2
1M context · IndexShare sparse attention · flagship MoE for long-horizon coding and Agents
GLM-5.2 is Zhipu AI (Z.ai)'s open-weight AI chat LLM released in June 2026, using a 744B-parameter MoE architecture (~40B activated)—the first GLM model to stably support 1M-token context. Optimized for long-horizon software engineering and Agent tasks, GLM-5.2 features IndexShare sparse attention and enhanced MTP speculative decoding, excelling at coding, tool calling, and multi-file collaboration, with tunable High / Max reasoning strength. Whether you are a developer, enterprise team, or AI engineer, you can learn about and integrate GLM-5.2 online for intelligent chat and automated development workflows.
Context window
1M tokens
Total parameters
744B MoE
Active parameters
~40B
Open license
MIT
💡 Why Choose
Why choose GLM-5.2?
GLM-5.2 is Z.ai's latest flagship chat model in the GLM series, released June 2026 and designed for long-horizon tasks. Compared with GLM-5.1, GLM-5.2 delivers leapfrog upgrades in long-horizon coding, Agent collaboration, and context scale—expanding context from 200K to a stable 1M tokens, making whole-repo analysis, marathon dev sessions, and complex multi-step Agent tasks practically callable.
GLM-5.2 uses a 744B-A40B MoE architecture with IndexShare sparse attention: every 4 Transformer layers share one lightweight indexer, reducing index-related compute by ~2.9× at 1M context. With enhanced MTP speculative decoding layers, GLM-5.2 balances reasoning quality and generation throughput in long-context scenarios. Released under MIT license, it is available via Z.ai API or self-hosted with vLLM / SGLang.
On bikabika AI, you can explore GLM-5.2 core capabilities, reasoning mode differences, typical use cases, and integration in one place to quickly assess whether it fits your coding, Agent, or enterprise AI chat needs—and try it online.
⚡ Features
GLM-5.2 core features
The six capabilities below form GLM-5.2's core competitive strengths in long-horizon coding and agents.
Stable 1M-token context
GLM-5.2 extends GLM series context from 200K to 1M tokens, stably supporting long-horizon tasks and processing large codebases, long documents, and full Agent session history in one go.
Long-horizon coding and software engineering
GLM-5.2 is trained for software engineering and multi-file collaboration, approaching frontier closed models on benchmarks like FrontierSWE—suited to cross-file refactors, code review, and marathon Agent development sessions.
IndexShare sparse attention
GLM-5.2's IndexShare architecture shares a lightweight indexer every 4 Transformer layers, cutting indexer-related compute by about 2.9× at 1M context for more efficient long-context inference.
High / Max reasoning strength
GLM-5.2 supports a reasoning_effort parameter: Max is the default deep-reasoning mode for complex Agents and multi-step planning; High balances performance and latency better.
Tool calling and Agent workflows
GLM-5.2 is optimized for Agent scenarios with stable function calling and tool selection, maintaining state consistency on long-horizon tasks and reducing interruptions from frequent context compression.
MIT open weights for self-hosting
GLM-5.2 is released under the MIT license. Call it online via Z.ai API or self-host with vLLM, SGLang, and similar frameworks for enterprise compliance and private deployment.
Want to experience GLM-5.2 million-context coding yourself?
GLM-5.2 supports Z.ai API and open self-deployment—start your first long-horizon AI chat experience now.
🔄 Compare
GLM-5.2 reasoning intensity comparison
GLM-5.2 supports High and Max reasoning_effort tiers—the comparison below helps you choose between complex agents and efficient calls.
| Dimension | Max reasoning mode | High reasoning mode |
|---|---|---|
| Best for | Complex coding, multi-step agents, long-horizon planning | Daily development, high-frequency calls |
| Reasoning depth | Deeper (default mode) | More balanced |
| Response latency | Higher (quality-first) | Relatively lower |
| Agent performance | More stable multi-step tool calls | More efficient for lightweight tasks |
| Coding tasks | Cross-file refactors, complex review | Local completion, fast iteration |
| Parameter setting | reasoning_effort default / Max | reasoning_effort="high" |
| Context | Supports 1M tokens (GLM-5.2[1m]) | Supports 1M tokens (GLM-5.2[1m]) |
| Recommended users | Agent engineers, architects | Daily developers, bot operators |
💎 Highlights
GLM-5.2 technical highlights
- GLM-5.2 stably supports 1M-token context—a major leap for long-horizon tasks in the GLM series
- GLM-5.2's 744B-A40B MoE with ~40B activated balances flagship capability and inference efficiency
- GLM-5.2 IndexShare cuts sparse-attention indexer overhead by about 2.9× at million-token context
- GLM-5.2 enhanced MTP speculative decoding boosts acceptance length by up to ~20% for better generation throughput
- GLM-5.2 High / Max reasoning strength flexibly switches complex Agents and efficient calls
- GLM-5.2 MIT open license + Z.ai API supports cloud calls and local deployment
🎯 Use Cases
GLM-5.2 use cases
From monorepo refactors to enterprise private deployment, GLM-5.2 delivers stable, efficient long-context AI chat and reasoning across the six scenarios below.
Developers · Architects
Large monorepo code analysis
GLM-5.2's 1M-token context can load core modules of medium-to-large monorepos in one pass—reducing frequent summary compression while maintaining global consistency in cross-file dependency analysis, refactor design, and code review.
- 1M tokens
- Monorepo
- GLM-5.2
AI engineers · Technical teams
Long-horizon coding agent sessions
GLM-5.2 is trained on Agent trajectories—maintaining tool call coherence in long sessions, ideal for multi-file collaboration and automated development in OpenCode, GLM Coding Plan, and similar scenarios.
- Coding agents
- Tool calling
- GLM-5.2
Developers · Automation engineers
Function calling and automation workflows
GLM-5.2 accurately selects tools, passes valid parameters in function calling, and continues reasoning after results—ideal for Agent pipelines connecting APIs, databases, and internal systems.
- Function calling
- Agent workflow
- Z.ai API
Legal · Analysts
Ultra-long documents and compliance processing
GLM-5.2's million-token context suits one-pass analysis and structured summarization of long contracts, compliance materials, and research reports—avoiding information fragmentation from chunked processing.
- Long documents
- Compliance
- GLM-5.2
Enterprise IT · Ops teams
MIT open self-hosted deployment
GLM-5.2 weights are released under MIT—enterprises can obtain models from Hugging Face / ModelScope and deploy privately with vLLM or SGLang for data compliance and security.
- MIT open source
- Self-hosting
- vLLM
Indie developers · Small teams
GLM Coding Plan development scenarios
GLM Coding Plan subscribers can enable GLM-5.2 and GLM-5.2[1m] million-context directly—choose High / Max reasoning intensity per task for efficient daily coding and Agent development.
- GLM Coding Plan
- Z.ai
- GLM-5.2
📖 Guide
How to use GLM-5.2
Follow the steps below to get started quickly and complete your first long-horizon coding or Agent task with GLM-5.2.
-
Choose reasoning intensity
Pick reasoning strength by task: use Max (default) for complex multi-step planning and Agents; set reasoning_effort to High when latency matters more.
-
Write your prompt
For 1M context, use the GLM-5.2[1m] model id in the API or Claude Code, and pass complete codebases or documents in one go when possible.
-
Configure API parameters
Write structured system prompts with clear role, tool permissions, and output format—GLM-5.2 responds more stably to clear task descriptions and engineering traces.
-
Iterate and refine
For Agents, fully retain assistant tool calls and reasoning in conversation history; call via Z.ai API or self-hosted vLLM / SGLang.
❓ FAQ
GLM-5.2 FAQ
Below are the most common questions about GLM-5.2 features, context, open deployment, and integration.
What is GLM-5.2, and who is it for?
GLM-5.2 is Zhipu AI (Z.ai)'s open-weight AI chat LLM for long-horizon software engineering, coding Agents, and tool-driven automation. It suits developers, AI engineers, technical teams handling large codebases or long documents, and enterprises wanting MIT open weights for self-deployment.
How large is GLM-5.2's context window?
GLM-5.2 stably supports 1M-token context—a major upgrade from GLM-5.1's 200K. Enable full million-token context with the GLM-5.2[1m] model ID on supported platforms for whole-repo analysis and long-horizon Agent sessions.
How do High and Max reasoning modes differ in GLM-5.2?
Controlled via reasoning_effort: Max is default with deeper reasoning for complex coding, multi-step Agents, and long-horizon planning; High uses a more conservative reasoning budget for a better quality–latency balance in everyday development and high-frequency calls.
What upgrades does GLM-5.2 bring vs. GLM-5.1?
Core upgrades vs. GLM-5.1: context from 200K to stable 1M tokens, IndexShare sparse attention lowering long-context compute, enhanced MTP speculative decoding for throughput, and stronger long-horizon coding and Agent capability.
Is GLM-5.2 open source? Can I self-host it?
Yes. GLM-5.2 releases open weights under MIT, available on Hugging Face and ModelScope, and can be self-hosted with vLLM, SGLang, and similar inference frameworks—or called online via the official Z.ai API.
How do I access GLM-5.2?
Set the model name to GLM-5.2 via Z.ai API for online calls; use GLM-5.2[1m] when you need 1M context. GLM Coding Plan subscribers can enable it directly. Self-hosters download weights and configure vLLM or SGLang per official docs.
💡 Tips
GLM-5.2 prompt and integration tips
Use Max reasoning mode (default) for complex multi-step agents, cross-file refactors, and long-horizon planning; set reasoning_effort="high" for daily completion and high-frequency calls.
When enabling full 1M context, use GLM-5.2[1m] model ID and pass complete codebases or documents in one request to reduce intermediate compression.
For Agent sessions, fully preserve assistant tool calls and reasoning in history so GLM-5.2 maintains state coherence in long-horizon tasks.
Self-hosted users should configure vLLM or SGLang per official docs; cloud users can connect quickly via Z.ai API with model set to GLM-5.2.
Specify engineering constraints, directory structure, and output format in system prompts—GLM-5.2 responds more reliably to structured software engineering instructions.
Ready to try GLM-5.2?
Get started now and unlock your AI creative potential with GLM-5.2