Developers are the primary consumers of LLM APIs, and Chinese models like DeepSeek-V4, GLM-4, and Qwen3 have evolved into powerful coding assistants. Through TokenEase's unified API, engineering teams can integrate these models into their development workflows—from generating boilerplate code to automating entire test suites.
This guide covers six high-impact applications of Chinese LLMs for developer productivity, with production-ready Python examples you can deploy today.
Switch between DeepSeek-V4 (best for complex algorithms), GLM-4 (excellent for Chinese documentation), and Qwen3 (strong reasoning for architecture decisions) with a single API key. All at 40% lower cost than direct provider pricing.
Chinese LLMs excel at generating production-ready code from natural language descriptions. Whether you need a REST API endpoint, a React component, or a data pipeline, models like DeepSeek-V4 can produce clean, idiomatic code in seconds.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are an expert Python developer. Generate production-ready FastAPI code with proper error handling, input validation, and type hints."},
{"role": "user", "content": "Create a FastAPI endpoint for user registration with email validation, password hashing with bcrypt, and JWT token generation. Include Pydantic models and proper HTTP status codes."}
],
"temperature": 0.2,
"max_tokens": 2000
}
)
code = response.json()["choices"][0]["message"]["content"]
print(code)
# Save to file: with open('auth_endpoint.py', 'w') as f: f.write(code)Best Practice: Use temperature 0.1-0.3 for code generation to ensure deterministic, predictable outputs. Set a high max_tokens (2000+) for complex functions.
Writing comprehensive test suites is time-consuming but critical. LLMs can analyze your functions and generate edge-case tests, mock setups, and parameterized test cases automatically.
import requests
function_code = """
def calculate_discount(price: float, coupon_code: str) -> float:
discounts = {"SAVE10": 0.10, "SAVE20": 0.20, "VIP": 0.30}
if coupon_code not in discounts:
return price
return price * (1 - discounts[coupon_code])
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Generate comprehensive pytest unit tests including edge cases, invalid inputs, boundary conditions, and parameterized tests. Include docstrings and arrange-act-assert structure."},
{"role": "user", "content": f"Generate pytest tests for this function:\n{function_code}"}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
test_code = response.json()["choices"][0]["message"]["content"]
print(test_code)Pro Tip: Feed the LLM your existing test conventions and fixtures for consistent style across your codebase.
Before human review, LLMs can catch common issues: security vulnerabilities, performance bottlenecks, style violations, and logic errors. This reduces review cycles and improves code quality.
import requests
code_to_review = """
def process_user_input(user_id, query):
conn = sqlite3.connect('app.db')
cursor = conn.cursor()
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
result = cursor.fetchall()
eval(query)
return result
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a senior security engineer. Review code for SQL injection, XSS, CSRF, command injection, insecure deserialization, and other OWASP Top 10 vulnerabilities. Provide severity ratings and fix suggestions."},
{"role": "user", "content": f"Review this code for security issues:\n```python\n{code_to_review}\n```"}
],
"temperature": 0.2,
"max_tokens": 2000
}
)
review = response.json()["choices"][0]["message"]["content"]
print(review)
# Expected output: SQL injection (CRITICAL), eval() usage (CRITICAL), missing input validationKeeping documentation in sync with code is a perpetual challenge. LLMs can generate docstrings, API documentation, README files, and changelogs directly from source code.
import requests
api_code = """
class PaymentGateway:
def process_payment(self, amount: Decimal, currency: str,
card_token: str, metadata: dict = None) -> PaymentResult:
'''Process a payment through the gateway.'''
validated = self._validate_amount(amount, currency)
if not validated:
raise InvalidAmountError("Amount exceeds limit")
return self._charge(card_token, amount, currency, metadata)
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Generate comprehensive Chinese API documentation in Markdown format. Include parameter descriptions, return value specifications, exception lists, usage examples, and version history. Use professional technical Chinese."},
{"role": "user", "content": f"为以下Python类生成中文API文档：\n{api_code}"}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
docs = response.json()["choices"][0]["message"]["content"]
print(docs)
# Save: with open('api_docs_zh.md', 'w', encoding='utf-8') as f: f.write(docs)When bugs are reported, LLMs can analyze stack traces, error logs, and source code to suggest fixes. This accelerates debugging cycles, especially for complex, multi-file issues.
import requests
error_context = """
Error: KeyError: 'user_profile'
File: /app/services/auth.py, Line 145
Traceback:
File "/app/services/auth.py", line 145, in get_user_context
profile = cache.get(session['user_profile'])
File "/app/cache/redis_client.py", line 89, in get
return json.loads(self.redis.get(key))
Related Code (auth.py lines 140-150):
def get_user_context(session_id):
session = session_store.get(session_id)
profile = cache.get(session['user_profile'])
preferences = cache.get(session['user_prefs'])
return merge(profile, preferences)
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "You are a debugging expert. Analyze error traces, identify root causes, suggest specific code fixes with line numbers, and recommend preventive measures. Consider race conditions, null pointers, and cache invalidation issues."},
{"role": "user", "content": f"Debug this error and suggest fixes:\n{error_context}"}
],
"temperature": 0.2,
"max_tokens": 2000
}
)
fix = response.json()["choices"][0]["message"]["content"]
print(fix)Designing clean, consistent APIs requires careful consideration of endpoints, request/response schemas, error handling, and versioning. LLMs can generate OpenAPI specs, GraphQL schemas, and gRPC definitions from business requirements.
import requests
requirements = """
Design a REST API for an e-commerce order management system with these features:
- Create order with items, shipping address, payment method
- Update order status (pending -> confirmed -> shipped -> delivered -> cancelled)
- Query orders with filters (date range, status, customer ID)
- Cancel order with reason and refund processing
- Webhook notifications for status changes
- Support pagination and sorting
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Generate a complete OpenAPI 3.0 specification in YAML format. Include all endpoints, request/response schemas, authentication, error responses, examples, and pagination parameters. Follow REST best practices and use standard HTTP status codes."},
{"role": "user", "content": requirements}
],
"temperature": 0.2,
"max_tokens": 3000
}
)
openapi_spec = response.json()["choices"][0]["message"]["content"]
print(openapi_spec)
# Save: with open('openapi_orders.yaml', 'w') as f: f.write(openapi_spec)Get your API key in 30 seconds and integrate DeepSeek-V4, GLM-4, and Qwen3 into your development workflow. Sign up free →