GLM-5.2 Master Prompts — Open Weights Frontier Model (June 2026)
Optimized prompts for Z.AI's GLM-5.2 — the #1 ranked open-weights model on Artificial Analysis. 753B MoE architecture, 1M context, MIT license. Outperforms GPT-5.5 on coding benchmarks at a fraction of the cost. Updated June 2026.
📋 Prompt
/* GLM-5.2 MASTER PROMPT VERSION: 1.0.0 RELEASED: June 2026 by Z.AI ARCHITECTURE: 753B MoE (40B active per token) LICENSE: MIT (Open Weights) CONTEXT: 1M tokens PRICE: $1.40/M input | $4.40/M output SPEED: 105 tok/s RANKING: #1 Intelligence Index (51), beats GPT-5.5 on coding */ **Deployment Options:** ┌─────────────────────────────────────────┐ │ ☁ API: Z.AI cloud API │ │ 🏠 Self-host: Hugging Face weights │ │ 📦 Local: ollama, llama.cpp, vLLM │ │ 🔓 License: MIT — unrestricted use │ └─────────────────────────────────────────┘ **Prompt Template:** ``` [SYSTEM] You are GLM-5.2, an open-weights frontier AI by Z.AI. Your strengths: coding, long-context analysis, structured reasoning. Output language: [ENGLISH | 中文 | CODE] Output format: [TEXT | JSON | MARKDOWN] [CONTEXT] Domain: [PROGRAMMING | RESEARCH | ANALYSIS | GENERAL] Reference materials: [PATHS | CONTENT] [TASK] 1. [PRIMARY_GOAL] 2. [QUALITY_REQUIREMENTS] 3. [OUTPUT_SPECIFICATION] [CONSTRAINTS] - Time: [DEADLINE_IF_ANY] - Format: [SCHEMA_IF_STRUCTURED] - Scope: [BOUNDARIES] ``` **Coding Prompt Pattern (GLM-5.2 Specialty):** ``` Problem: [DESCRIPTION] Language: [PYTHON | TYPESCRIPT | RUST | GO | ...] Requirements: - [REQ_1] - [REQ_2] Constraints: [TIME_COMPLEXITY] [SPACE_COMPLEXITY] Deliverables: 1. Algorithm explanation with complexity analysis 2. Complete implementation with type annotations 3. Test suite (normal + edge + error cases) 4. Usage examples ``` **Long-Context Pattern:** ``` Context size: [N] tokens of [TYPE: codebase | papers | logs | docs] Phase 1 — INDEX: Build a topic map. Output: table of contents with relevance scores. Phase 2 — DEEP READ: For top-N sections, extract key claims and evidence. Phase 3 — SYNTHESIZE: Cross-reference findings, identify patterns and gaps. Phase 4 — REPORT: Structured output with citations. ``` **Model-Specific Optimizations:** - MoE architecture means 40B active params — efficient inference even at 753B total - 105 tok/s throughput — suitable for real-time applications - MIT license — use commercially, fine-tune, distribute freely - 1M context — process entire codebases, book-length documents - Outperforms GPT-5.5 on coding — use it for programming tasks first
💡 Tips
- GLM-5.2 is the #1 coding model — lead with programming tasks before general knowledge
- 1M token context window: upload entire codebases for holistic refactoring
- MIT license means zero restrictions — self-host, fine-tune, commercialize freely
- 105 tok/s speed enables real-time applications — no streaming lag
- For 中文 (Chinese) tasks: GLM-5.2 has native bilingual support
- Cost advantage: 4x cheaper than GPT-5.5 for equivalent coding quality
- Self-hosting: use vLLM for production, ollama for local dev
GLM-5.2 — The Open Weights Coding Powerhouse
Released June 2026 by Z.AI, GLM-5.2 is the #1 ranked open-weights model on the Artificial Analysis Intelligence Index (score: 51). It’s a 753B parameter Mixture-of-Experts model with 40B active parameters per token, offering frontier performance under an MIT license.
Why GLM-5.2 Matters
| Feature | GLM-5.2 | GPT-5.5 | Advantage |
|---|---|---|---|
| Coding Benchmarks | #1 | #2-3 | 🏆 GLM-5.2 |
| Price (per 1M tokens) | $1.40 / $4.40 | $15 / $60 | 💰 4-10x cheaper |
| Speed (tok/s) | 105 | ~80 | ⚡ 30% faster |
| Context Window | 1M | 128K | 📚 8x larger |
| License | MIT | Proprietary | 🔓 Total freedom |
| Self-Hosting | ✅ Yes | ❌ No | 🏠 Private deployment |
Prompting Strategy
- Coding first. GLM-5.2’s strongest domain is programming. Structure coding prompts like competitive programming problems — clear spec, constraints, expected output format.
- 1M context is real. Upload entire codebases, book-length documents, or full log files for holistic analysis.
- Structured output shines. GLM-5.2 produces excellent JSON when you define the schema upfront.
- Chinese and English. Native bilingual support — no translation overhead for mixed-language tasks.
Self-Hosting Quick Start
# Via ollama
ollama pull glm5.2
# Via vLLM (production)
python -m vllm.entrypoints.openai.api_server \
--model z-ai/glm-5.2 \
--tensor-parallel-size 8
# Via Hugging Face
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("z-ai/glm-5.2")
Cost Comparison
For a 100K-token coding task (input) producing 10K tokens (output):
- GLM-5.2: $0.14 + $0.04 = $0.18
- GPT-5.5: $1.50 + $0.60 = $2.10
- Savings: 91% cheaper with better coding quality
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