GLM-5.1 API Guide

Why GLM-5.1 for Chinese Content?

GLM-5.1 (from Zhipu AI / Tsinghua University) is the leading Chinese language model. While GPT-4o handles Chinese adequately, GLM-5.1 understands the nuances, idioms, and cultural context that Western models often miss:

• Native fluency - Trained primarily on Chinese corpora
• Cultural context - Understands idioms, historical references, social norms
• Business formal - Excellent at formal business Chinese (公文)
• Structured output - Reliable JSON and data extraction
• CEval score: 86.5% - Highest among all models on Chinese benchmarks

GLM-5.1 vs GPT-4o on Chinese Tasks

Task	GLM-5.1	GPT-4o
Classical poetry	Perfect meter	Rhyme errors
Business writing (公文)	Native fluency	Overly casual
Idiom usage	Perfect context	Occasional misuse
News article writing	Professional tone	Good but formal
Legal document analysis	Accurate nuances	Misses subtleties
CEval benchmark	86.5%	71.2%

API Setup

from openai import OpenAI

client = OpenAI(
    base_url="https://tokenease.io/v1",
    api_key="your_tokenease_api_key"
)

# Use GLM for Chinese content
response = client.chat.completions.create(
    model="zhipu",
    messages=[{"role": "user", "content": "写一篇关于人工智能的短文"}]
)

Chinese Content Creation

1. Marketing Copy

def generate_chinese_ad(product, audience, tone="professional"):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": f"你是专业文案写手。用{tone}的语气撰写中文营销文案。"},
            {"role": "user", "content": f"为{product}撰写面向{audience}的营销文案，包含标题、卖点和号召行动。"}
        ],
        max_tokens=1000
    )
    return response.choices[0].message.content

# Usage
ad = generate_chinese_ad(
    product="智能办公软件",
    audience="中小企业管理者",
    tone="专业且亲和"
)
print(ad)

2. Social Media Content

def create_weibo_post(topic, style="engaging"):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": "你是社交媒体运营专家。撰写吸引人的微博文案，包含话题标签。"},
            {"role": "user", "content": f"关于{topic}的微博文案，风格：{style}"}
        ],
        max_tokens=500
    )
    return response.choices[0].message.content

# Generate 5 posts
for topic in ["远程办公", "AI工具", "效率提升", "创业心得", "团队协作"]:
    print(f"\n【{topic}】")
    print(create_weibo_post(topic))

3. Technical Documentation

def translate_tech_docs(english_text):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": "你是技术文档翻译专家。将英文技术文档翻译成地道的中文，保持术语准确。"},
            {"role": "user", "content": english_text}
        ],
        max_tokens=2000
    )
    return response.choices[0].message.content

# Translate API documentation
docs = """
## Authentication
All API requests must include an Authorization header with your API key.
Example: Authorization: Bearer sk-...
"""

chinese_docs = translate_tech_docs(docs)
print(chinese_docs)

Business Applications

1. Contract Review

def review_contract(contract_text):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": "你是法律顾问。审查合同条款，识别风险点、模糊表述和不公平条款。"},
            {"role": "user", "content": f"请审查以下合同：\n\n{contract_text}"}
        ],
        max_tokens=2000
    )
    return response.choices[0].message.content

# Usage
contract = """
合同条款示例...
"""
review = review_contract(contract)
print(review)

2. Customer Service Response

def customer_service_reply(inquiry, customer_tier="standard"):
    tones = {
        "standard": "专业、礼貌",
        "vip": "专业、热情、重视",
        "enterprise": "专业、正式、解决方案导向"
    }
    
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": f"你是客服专员。用{tones.get(customer_tier, '专业、礼貌')}的语气回复客户。"},
            {"role": "user", "content": f"客户咨询：{inquiry}"}
        ],
        max_tokens=800
    )
    return response.choices[0].message.content

Structured Data Extraction

GLM-5.1 excels at extracting structured data from Chinese text:

import json

def extract_entities(chinese_text):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": "从文本中提取实体。返回JSON格式：{'people': [], 'organizations': [], 'locations': [], 'dates': []}"},
            {"role": "user", "content": chinese_text}
        ],
        max_tokens=1000
    )
    
    try:
        return json.loads(response.choices[0].message.content)
    except:
        return {"error": "Failed to parse"}

# Extract from news article
news = """
阿里巴巴今日宣布，张勇将卸任集团CEO，由吴泳铭接任。
公司总部设在杭州，此次人事变动将于9月10日生效。
"""

entities = extract_entities(news)
print(json.dumps(entities, ensure_ascii=False, indent=2))
# Output: {"people": ["张勇", "吴泳铭"], "organizations": ["阿里巴巴"], "locations": ["杭州"], "dates": ["9月10日"]}

Sentiment Analysis in Chinese

def analyze_chinese_sentiment(text):
    response = client.chat.completions.create(
        model="zhipu",
        messages=[
            {"role": "system", "content": "分析中文文本情感。返回JSON：{'sentiment': 'positive/negative/neutral', 'score': 0-1, 'keywords': []}"},
            {"role": "user", "content": text}
        ],
        max_tokens=500
    )
    return response.choices[0].message.content

# Analyze product reviews
reviews = [
    "这个产品真的太棒了，强烈推荐！",
    "一般般吧，没有想象中好。",
    "太差了，完全不符合描述，退款！"
]

for review in reviews:
    result = analyze_chinese_sentiment(review)
    print(f"评论：{review}")
    print(f"分析：{result}\n")

Pricing

Model	Input/M	Output/M
GLM-5.1	$0.75	$0.75
GLM-5.1 Flash	$0.40	$0.40