Model Overview
GLM-4.6 is Zhipu AI’s flagship open-source reasoning model, officially released on September 30, 2025. It is currently the world’s strongest fully open-source long-context reasoning model, achieving near-parity with Claude Sonnet 4 on real-world coding and agentic tasks while offering superior efficiency and local deployment options.
- Architecture: 355B-scale Mixture-of-Experts (MoE)
- Total parameters: 355 billion
- Active parameters per inference: ~32 billion
- Pre-training: Massive high-quality tokens + advanced reinforcement learning for reasoning, coding, and tool-use alignment
Key capabilities that put it ahead of all other open-source models and on par with leading closed-source models:
- Native context length: 200K input tokens
- Real-world verified: stably processes extended multi-turn sessions with ~15–30% lower token consumption than predecessors, minimal degradation in long contexts
- Tool calling: reliably executes hundreds of consecutive tool calls with integrated reasoning support, enabling low-drift complex agent workflows
- Unique transparent reasoning: supports structured chain-of-thought and tool integration during inference — ideal for finance, law, code auditing, agents, and research where explainability, reliability, and efficiency are critical.
How to Use (OpenAI-compatible, works globally)
Python
from openai import OpenAI
BASE_URL = "https://inference.canopywave.io/v1"
API_KEY = os.environ.get("CANOPYWAVE_API_KEY")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
response = client.chat.completions.create(
model="zai/glm-4.6",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "please tell me a story."}
],
)
print(response.choices[0].message.content)Killer Use Cases
| Scenario | Typical Size | Why GLM-4.6 Wins |
| Full codebase audit & frontend polish | 100K–300K LOC | One-shot architecture + polished UI code + refactor plan |
| Competitive math / real-world coding | Full contest or multi-turn tasks | Near-parity with Claude Sonnet 4 on CC-Bench (48.6% win rate) |
| 400–600 page legal/financial docs | 500K–800K characters | Efficient long-context extraction + structured summary |
| Multi-day autonomous agents | 200–500 tool calls | Low token drift + native tool reasoning over long sessions |
| Academic research & code generation | 100+ papers or complex repos | Precise reasoning, tool integration, reproducible outputs |
Prompting Best Practices
1. Force visible reasoning (essential for coding, agents, research)
You are a world-leading expert. Always think step-by-step inside <thinking> tags before giving the final answer. Use tools when needed.
2. Highest-reliability pattern
Message 1: “Provide a complete step-by-step plan only — do NOT execute yet.”
Message 2: “Now execute the approved plan exactly.”
3. Recommended settings
- Coding / reasoning / agents → temperature=0.0–0.3
- Creative tasks → temperature=0.7–1.0
- Always include “Think step-by-step” in system prompt
Pricing & Limits
| Item | Detail |
| Official API | ~$0.45 / M input tokens, ~$1.50 / M output tokens |
| Max context | 200K input, 128K output |
| Knowledge cutoff | Mid-2025 |
Quick Links (All accessible globally)
- GLM 4.6 model card: GLM 4.6 API
- Get Start Now: Canopy Wave
- Model weights (open-source): zai-org/GLM-4.6 · Hugging Face
Try it once on a real 200K-token codebase, a complex agent workflow, or a competitive programming set. You’ll instantly see why GLM-4.6 became the go-to open frontier model for developers and researchers worldwide within weeks of release.
Welcome to the open-source coding and reasoning ceiling. Enjoy!