GLM-4.7 Flash
by Z.ai · Released 2025
Zhipu AI's (Z.ai) fast inference model from the GLM-4 family. Built on the General Language Model architecture with strong bilingual (Chinese/English) capabilities. Optimized for speed with reliable tool-call instructions and concise responses.
GLM-4.7 Flash
Powered by Z.ai · General Language Model (GLM), Transformer
Context Window
128K
Parameters
32B
Max Output
8K
Category
LLM Chat
Overview
GLM-4.7 Flash is Zhipu AI's (Z.ai) fast inference model from the GLM-4 family, featuring a 32B parameter dense architecture optimized for speed and reliability. Built on the General Language Model (GLM) architecture, it delivers strong bilingual Chinese/English performance with particularly reliable tool-calling and function-calling capabilities.
The model is designed for production workloads that require fast, concise responses with reliable structured output. Its tool-calling implementation is notably robust — it follows function-calling instructions precisely, making it an excellent choice for building AI agents that need to interact with external APIs and services. The 128K context window handles substantial documents and conversations.
GLM-4.7 Flash is open-source and available on HuggingFace, making it one of the strongest open bilingual models available. It is particularly well-suited for Chinese language applications, bilingual customer support, and any workflow that requires reliable tool use with fast inference. The model's concise response style makes it efficient for production deployments where token costs matter.
Pricing
| Metric | Price |
|---|---|
| Input /1M tokens | ₹50.0000 |
| Output /1M tokens | ₹200.0000 |
1 credit = ₹1 = $0.01 USD. Prices shown from provider; CallMissed passes through with ~35% markup.
Key Highlights
- Fast inference with concise, natural responses
- Strong bilingual Chinese/English performance
- Reliable tool-call and function-calling support
- Open-source model available on HuggingFace
Benchmarks
| Benchmark | Score |
|---|---|
| C-Eval | 82.3% |
| MMLU | 78.5% |
| HumanEval | 82.1% |
| GSM-8K | 88.7% |
| Tool-Call Accuracy | 94.2% |
Technical Details
- Architecture: General Language Model (GLM) with 32B dense parameters
- Strong bilingual Chinese/English performance
- Reliable tool-calling and function-calling implementation
- Context window: 128K tokens
- Open-source — available on HuggingFace under permissive license
- Optimized for fast inference with concise response style
- Available via Zhipu AI API and CallMissed unified gateway
Strengths
- Best-in-class bilingual Chinese/English performance at 32B scale
- Exceptionally reliable tool-calling and function-calling
- Open-source on HuggingFace — can be self-hosted and fine-tuned
- Fast inference with concise, efficient responses
Limitations
- Primarily optimized for Chinese/English — weaker on other languages
- 32B dense model is less efficient than MoE architectures at similar quality
- Smaller community and ecosystem compared to Llama or Qwen families
Use Cases
API Example
curl https://api.callmissed.com/v1/chat/completions \
-H "Authorization: Bearer cm_YOUR_KEY" \
-d '{"model": "glm-4.7-flash", "messages": [{"role": "user", "content": "Write a Python function to parse JSON"}]}'Endpoint: POST /v1/chat/completions · Model ID: glm-4.7-flash
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