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How to Use GLM-4.7 for Free: A Complete Guide

BlogsGLM-4.7AIFree TierAbout 7 min

How to Use GLM-4.7 for Free: A Complete Guide

GLM-4.7, the latest open-source large language model from Zhipu AI (Z.ai), has taken the AI community by storm. With 355B total parameters (32B active), a massive 200K context window, and remarkable coding capabilities—achieving 73.8% on SWE-bench—it's positioned as a powerful alternative to proprietary models like Claude Sonnet 4.5. The best part? You can access GLM-4.7 for free through multiple platforms. This guide will walk you through all the legitimate ways to use GLM-4.7 without spending a dime.

Why GLM-4.7 is Worth Trying

GLM-4.7 represents a significant leap forward in open-source AI:

  • Outstanding coding performance: 73.8% on SWE-bench, 84.9% on LiveCodeBench
  • Massive context window: 200K tokens for complex, long-context tasks
  • Preserved Thinking: Retains reasoning blocks across conversations for better continuity
  • MIT-licensed: Fully open-source for commercial use
  • Multilingual support: Excels in both English and Chinese tasks
  • Tool use capabilities: 87.4% on τ²-Bench for agentic workflows
  • Cost-effective: Significantly cheaper than closed-source alternatives

Method 1: OpenRouter Free Credits

What You Get

OpenRouter provides a unified API for multiple AI models, including GLM-4.7, with a free tier for experimentation.

Step-by-step access:

  1. Visit openrouter.ai
  2. Create a free account
  3. Navigate to "Account Settings" and generate your API key
  4. Check the models page for GLM-4.7 availability (marked as zai/glm-4.7 or similar)
  5. Use the OpenAI-compatible SDK with OpenRouter's base URL

Free Tier Features (as of April 2025):

  • 50 requests/day on free model variants
  • 20 requests/minute rate limit
  • Expandable to 1000 requests/day with $10 minimum balance

Sample API Usage:

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="your_openrouter_api_key"
)

response = client.chat.completions.create(
  model="zai/glm-4.7",
  messages=[{"role": "user", "content": "Write a Python function to sort an array"}],
  max_tokens=1000
)

print(response.choices[0].message.content)

Pro Tips:

  • Monitor your usage in the OpenRouter dashboard to stay within free limits
  • Use GLM-4.7 for coding tasks where it excels
  • Combine requests to minimize API calls when possible

Method 2: Vercel AI Gateway

Free Access Through Vercel

Vercel has integrated GLM-4.7 into its AI Gateway, offering developers seamless access.

Setup Process:

  1. Go to vercel.com and create a free account
  2. Create a new project or use an existing one
  3. Navigate to the AI Gateway settings
  4. Add GLM-4.7 as a provider (model ID: zai/glm-4.7)
  5. Use Vercel AI SDK for easy integration

Example with Vercel AI SDK:

import { generateText } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';

const glm = createOpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
});

const result = await generateText({
  model: glm('zai/glm-4.7'),
  prompt: 'Explain how Mixture-of-Experts architecture works',
});

console.log(result.text);

Benefits:

  • Built-in rate limiting and caching
  • Easy integration with Next.js projects
  • Free tier available for hobby projects
  • Streamlined deployment workflow

Method 3: Hugging Face Inference API

Free Inference Access

Hugging Face hosts GLM-4.7 with free inference API access for experimentation.

Getting Started:

  1. Visit huggingface.co/zai-org/GLM-4.7
  2. Sign up for a free Hugging Face account
  3. Accept the model's user agreement (if required)
  4. Generate an access token in your settings
  5. Use the Inference API endpoint

API Example:

import requests

API_URL = "https://api-inference.huggingface.co/models/zai-org/GLM-4.7"
headers = {"Authorization": "Bearer your_hf_token"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
output = query({
	"inputs": "Write a detailed explanation of machine learning concepts",
})

Free Tier Limitations:

  • Rate limits: approximately 300 requests/hour
  • Queue times may vary based on server load
  • Best suited for experimentation and prototyping

Method 4: Local Deployment with GGUF

Run GLM-4.7 Locally

For complete privacy and unlimited usage, you can run quantized versions of GLM-4.7 locally using GGUF format.

Prerequisites:

  • A computer with sufficient RAM (32GB+ recommended for comfortable usage)
  • Ollama or llama.cpp installed
  • Download the GGUF model from Hugging Face

Using Ollama:

# Create a Modelfile for GLM-4.7
echo "FROM ./GLM-4.7-GGUF/glm-4.7.Q4_K_M.gguf" > Modelfile
echo "PARAMETER temperature 0.7" >> Modelfile
echo "PARAMETER top_p 0.9" >> Modelfile
echo "PARAMETER num_ctx 200000" >> Modelfile

# Create the model
ollama create glm-4.7 -f Modelfile

# Run the model
ollama run glm-4.7 "Write a Python script for data analysis"

Using llama.cpp:

# Download and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# Run the model
./main -m GLM-4.7-GGUF/glm-4.7.Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms" \
  -n 512 \
  -c 200000

Advantages:

  • Complete privacy (data never leaves your machine)
  • No rate limits or API costs
  • Customizable quantization levels
  • Can be used offline

Hardware Requirements:

  • Minimum: 16GB RAM for 4-bit quantization
  • Recommended: 32GB+ RAM for smoother experience
  • GPU acceleration optional but recommended for faster inference

Method 5: OpenCode AI Chat

Conversational Access Through OpenCode

OpenCode provides a user-friendly chat interface for interacting with AI models, including GLM-4.7.

Access Steps:

  1. Visit the OpenCode platform
  2. Start a new conversation
  3. Select GLM-4.7 from the model dropdown (if available)
  4. Begin chatting with the model

Use Cases:

  • Quick coding assistance
  • Debugging help
  • Code explanations
  • Learning programming concepts

Benefits:

  • No API key required
  • Intuitive chat interface
  • Ideal for non-technical users
  • Perfect for experimentation

Method 6: Z.ai Official Platform

Direct Access from the Source

Z.ai, the creator of GLM-4.7, offers direct access to their models through their platform.

Getting Started:

  1. Visit z.ai
  2. Create a free account
  3. Navigate to the GLM-4.7 section
  4. Access the model through their web interface or API
  5. Check for any free tier or promotional offers

API Example:

import requests

API_URL = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
headers = {
  "Authorization": "Bearer your_zai_api_key",
  "Content-Type": "application/json"
}

payload = {
  "model": "glm-4.7",
  "messages": [
    {"role": "user", "content": "Help me understand neural networks"}
  ]
}

response = requests.post(API_URL, headers=headers, json=payload)
print(response.json())

Free Tier Information:

  • Z.ai typically offers free credits for new users
  • Check current promotions on their website
  • Free tier may have daily/monthly limits

Method 7: Puter.js Integration

Free, Serverless Access

Puter.js offers a unique "user-pays" model where you can access AI capabilities through their platform without API keys or server setup.

Getting Started:

  1. Include Puter.js in your HTML file:
<script src="https://js.puter.com/v2/"></script>
  1. Use GLM-4.7 through their interface:
puter.ai.chat(
  "Write a function to implement binary search",
  { model: "z-ai/glm-4.7" }
).then(response => {
  console.log(response);
  puter.print(response, {code: true});
});

Advantages:

  • No API keys required
  • User pays for their own usage
  • Perfect for client-side applications
  • No server infrastructure needed

Note: Check Puter's documentation for the latest supported models and availability of GLM-4.7.

Maximizing Your Free Usage

Smart Usage Strategies

1. Optimize Your Requests:

  • Use the right model size for the task
  • Be specific in your prompts to reduce token usage
  • Break complex tasks into smaller, focused queries

2. Implement Caching:

  • Cache responses for frequently asked questions
  • Use TTL (Time-to-Live) for cache invalidation
  • Reduce redundant API calls by up to 60%

3. Batch Operations:

  • Combine multiple related queries into single requests
  • Use batch processing for bulk operations
  • Minimize API overhead

4. Choose the Right Platform:

  • Use OpenRouter for API access with good free tier
  • Use Vercel AI Gateway for Next.js projects
  • Use Hugging Face for experimentation
  • Use local deployment for privacy and unlimited usage

Common Limitations and Solutions

Rate Limits:

  • Issue: Limited requests per minute/day on free tiers
  • Solution: Implement request queuing, use multiple platforms, or deploy locally

Context Window:

  • Issue: Some platforms may limit context in free tiers
  • Solution: Use GLM-4.7's full 200K context on platforms that support it, or use local deployment

Queue Times:

  • Issue: Free inference APIs may have wait times
  • Solution: Use during off-peak hours, or switch to local deployment

Performance Benchmarks

BenchmarkGLM-4.7 ScoreGPT-4oClaude Sonnet 4.5
SWE-bench73.8%71.8%72.0%
LiveCodeBench84.9%82.1%83.5%
τ²-Bench87.4%85.2%86.1%
Terminal Bench 2.041%38%39%

Data aggregated from multiple benchmark tests

Best Use Cases for GLM-4.7

1. Code Generation and Debugging:

  • Write production-quality code
  • Debug complex issues
  • Refactor existing code
  • Generate test cases

2. Agentic Workflows:

  • Use with Claude Code, Cline, or Roo Code
  • Implement automated coding assistants
  • Build AI-powered development tools

3. Multilingual Applications:

  • Support for English and Chinese
  • Code translation between languages
  • Localization tasks

4. Long-Context Reasoning:

  • Analyze large codebases
  • Review lengthy documentation
  • Process multi-file projects

Integration Examples

With Cursor (AI Code Editor):

// Configure Cursor to use GLM-4.7 via OpenRouter
// Settings → Models → Add Custom Model
Model ID: zai/glm-4.7
Base URL: https://openrouter.ai/api/v1
API Key: your_openrouter_key

With VS Code (Continue Extension):

// .vscode/settings.json
{
  "continue.model": "zai/glm-4.7",
  "continue.apiBaseUrl": "https://openrouter.ai/api/v1",
  "continue.apiKey": "your_openrouter_key"
}

Safety and Best Practices

API Key Security

  • Never commit API keys to version control
  • Use environment variables for storing credentials
  • Rotate keys regularly
  • Monitor usage for unauthorized access

Responsible Usage

  • Respect platform terms of service
  • Don't abuse free tiers for commercial purposes
  • Consider upgrading to paid plans for production use
  • Acknowledge the model in your projects

Data Privacy

  • Be aware of data retention policies on cloud platforms
  • Use local deployment for sensitive data
  • Review platform privacy policies
  • Implement data sanitization when needed

When to Consider Paid Plans

Signs You Need Paid Access:

  • Regularly hitting rate limits on free tiers
  • Need guaranteed availability for production
  • Require faster response times
  • Building commercial applications
  • Need advanced features like fine-tuning

Upgrade Options:

  • OpenRouter: Pay-as-you-go with competitive pricing
  • Z.ai Coding Plan: $3/month for Claude-level coding
  • Vercel Pro: Enhanced AI Gateway features
  • Self-hosting: Deploy on your own infrastructure

Hosting Recommendation:
For production deployments requiring scalability, consider LightNode's AI-optimized cloud solutions for hosting GLM-4.7 with dedicated GPU instances and seamless scaling.

Troubleshooting Common Issues

"Model not available" error:

  • Try during off-peak hours
  • Check if the model is supported on the platform
  • Switch to an alternative platform
  • Verify you're using the correct model ID

Rate limit exceeded:

  • Wait for the limit to reset
  • Implement request queuing
  • Use multiple API keys (if allowed)
  • Consider local deployment for high-volume usage

Memory issues with local deployment:

  • Use a more aggressive quantization (e.g., Q4_K_M instead of Q8_0)
  • Reduce context window size
  • Close other applications to free up RAM
  • Consider using GPU acceleration

Slow inference on local deployment:

  • Enable GPU acceleration if available
  • Use lower quantization levels
  • Reduce maximum tokens
  • Use a more powerful machine

Conclusion

GLM-4.7 offers exceptional capabilities for coding, reasoning, and agentic tasks—all accessible through multiple free tiers and open-source deployment options. Whether you're a developer looking for a Claude alternative, a researcher experimenting with state-of-the-art models, or a hobbyist exploring AI, there's a free access method that suits your needs.

Quick Start Recommendations:

  • Beginners: Start with OpenRouter or Hugging Face Inference API
  • Developers: Use Vercel AI Gateway for seamless integration
  • Privacy-focused users: Deploy locally using GGUF quantization
  • Experimenters: Try multiple platforms to find your favorite
  • Production users: Upgrade to paid tiers or self-host with LightNode

Remember: While free access is generous, consider supporting the platforms and open-source projects you find valuable by upgrading to paid plans, contributing to the community, or acknowledging GLM-4.7 in your work.

GLM-4.7 represents the democratization of powerful AI capabilities. By leveraging these free access methods, you can build, experiment, and innovate without financial barriers. The future of AI is open, and GLM-4.7 is leading the charge.

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