Important
📢 KEY ANNOUNCEMENT: GLM-5.2 is officially live! The model is immediately available across Z.ai Coding Plan subscriptions, and the complete model weights are scheduled to be officially open-sourced next week under the permissive MIT License.
GLM-5.2 is Z.ai's newly released flagship coding model. Built upon the strong foundations of the GLM-5 lineage, it introduces a massive 1-million usable context window (a 5x jump from GLM-5.1's 200K), a robust MIT open-source license for weights release, and continued strengths in long-horizon tasks (exceptional performance on long-horizon agentic workflows).
🔗 Official Resources:
- 📰 Official Blog & Announcement: Z.ai Announcement on X
- 💳 Pricing Structures: Z.ai Coding Plan Subscriptions
- 📖 Developer Usage Docs: Z.ai Devpack Documentation
Below is a detailed engineering spec matrix comparing the evolution of the GLM-5 family over the course of 2026:
| Specification | 🚀 GLM-5 (Feb 2026) | ⚡ GLM-5.1 (Apr 2026) | 🔥 GLM-5.2 (Jun 2026) |
|---|---|---|---|
| Parameter Size | ~744B | ~744B | ~744B (Est.) |
| Context Window | 128K | 200K | 1M (Usable) |
| Attention Mechanism | DeepSeek Sparse Attention | DeepSeek Sparse Attention | DeepSeek Sparse Attention |
| Training Infrastructure | Huawei Ascend | Huawei Ascend | Huawei Ascend |
| Primary Design Focus | NVIDIA-Free Frontier Feasibility | Long-Horizon Agentic Stability | High-Capacity Multi-File Context |
| Max Agent Run Time | ~2 Hours | ~8 Hours (600+ Iterations) | 8+ Hours (Optimized) |
| Open Source Licensing | MIT License | MIT License | MIT License (Weights next week) |
GLM-5.2 trades blows with frontier models, showing a massive leap in reasoning stability and code taste compared to its predecessor.
According to the AICodeKing Benchmark Review, the model has superb coding taste and excels at UX/logic. On this benchmark:
- GLM-5.2 [81.43] places above Opus 4.7 (55.71) and GPT-5.5 (38.57), trailing only slightly behind Opus 4.8 (87.14) and Fable 5 (88.57).
Fable 5 | ██████████████████████████████ 88.57
Opus 4.8 | █████████████████████████████░ 87.14
GLM-5.2 [81.43] | ████████████████████████████░░ 81.43 ✨ (AICodeKing Review)
Opus 4.7 | ███████████████████░░░░░░░░░░ 55.71
GPT-5.5 | ████████████░░░░░░░░░░░░░░░░░ 38.57
On the Code v3 logic and code execution leaderboard, GLM-5.2 shows remarkable category stability and fails zero runs compared to GLM-5.1:
| Model | Grade 1 | Grade 2 | Grade 3 | Grade 4 | Grade 5 | Failures / Total |
|---|---|---|---|---|---|---|
| GLM-5.2 (max) 🚀 | 16/B+ | 6/A | 8/A | 8/A | 43/C | 0 / 9 (Stable) |
| GPT-5.4 (high) | 9/A | 10/A | 8/B | 18/B | 58/C | 1 / 9 |
| GLM-5.1 | 20/C+ | 14/B | 15/B | 60/D | Failed (2/9) | 3 / 9 |
| DeepSeek V4 Pro (max) | 16/C | 8/B | 21/C | 36/C | Failed (0/9) | 0 / 9 |
GLM-5.2 is designed with a Claude Code compatibility layer. Developers can configure their devpack setups to use GLM-5.2 (max) for reasoning tasks.
To configure your local client for GLM-5.2 with the activated 1-million context window and prevent premature token compression, use the following configuration in your Devpack environment:
{
"ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-5.2[1m]",
"CLAUDE_CODE_AUTO_COMPACT_WINDOW": "1000000"
}Below is how Claude Code's native /effort commands map to GLM-5.2's backend reasoning states:
| Claude Code Effort | Backend GLM State | Recommended Use Case |
|---|---|---|
/effort low |
default |
Fast code completions and syntax edits |
/effort medium |
high |
Multi-file structure updates & test generation |
/effort high |
max |
Complex logic refactoring and algorithm design |
/effort max |
ultracode |
Deep, multi-step debugging and agent runs |
Tip
Switch to Max: We recommend switching to max effort for deeper reasoning, complex architectural shifts, or long-horizon agent loops.
Z.ai provides a structured tier list designed to balance compute speeds and token budgets:
| Tier | Monthly Price | Token Allowance / Limits | Compute Priority | Best For |
|---|---|---|---|---|
| Lite | $12.60 |
Baseline Capacity (~1B tokens/mo cap) | Shared / Best-Effort | Lightweight iteration on small repos |
| Pro | $50.40 |
5x Lite Usage Allowance | High / Priority Compute | Multi-file coding and active devs |
| Max | $112.00 |
20x Lite Usage Allowance | Dedicated / Unthrottled | Marathon agentic runs & teams |
Warning
While the Lite tier is highly affordable and can handle heavy volume, Z.ai throttles users on this tier during peak hours. If your coding workflows involve continuous agentic loops, upgrading to Pro or Max guarantees unthrottled generation speeds.
Based on developer feedback from r/LocalLLaMA and r/chutesAI, here is a breakdown of the model's strengths and current weaknesses:
| 🟢 Capabilities & Strengths | 🔴 Limitations & Concerns |
|---|---|
| Superb Code Taste: Code follows modern conventions, styling, and design patterns. | UI vs UX: Excels at UX and functional logic flows, but less specialized in front-end UI aesthetics. |
| One-Shot Performance: Excellent precision, reducing back-and-forth prompt iterations. | API/Chat Latency: Raw API access and Chat interfaces are delayed until next week. |
| Permissive MIT Weights: Community benefits from commercially usable weights. | Lack of Independent Benchmarks: Z.ai hasn't published paper details yet (community verification pending weight drop). |
| BYOK Friendly: Highly suited for custom, local, or decentralized deployments (e.g. Chutes). | Compute Throttling: Users on the Lite tier report severe API throttling during peak hours. |
┌────────────────────────────────────────────────────────┐
│ GLM-5 (Feb 2026) │
│ • 744B Params • Sparse Attention • Huawei Ascend │
└───────────────────────────┬────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ GLM-5.1 (Apr 2026) │
│ • 200K Context Window • 8h Autonomous Runs / Stability│
└───────────────────────────┬────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────┐
│ GLM-5.2 (Jun 2026) │
│ • 1M Context Window • High/Max Reasoning • MIT Weights│
└────────────────────────────────────────────────────────┘
GLM-5.2, GLM-5, Zai Coding Plan, Claude Code Integration, 1 Million Context LLM, Open Source Coding Model, MIT License LLM, AI Coding Agent, Huawei Ascend AI, Local LLM Coding, KingBench 3, Code v3 LLM Benchmark, Chutes AI, Open Weights, Developer Tooling
