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How to Integrate DeepSeek, GLM & Qwen APIs with Python in 2026

Published: July 8, 2026 • 10 min read • TokenEase Team

Chinese AI models like DeepSeek V4, GLM-5.1, and Qwen-Plus are now competitive with GPT-4 and Claude on many benchmarks — often at a fraction of the cost. But integrating them into your Python application can be challenging: each provider has its own API format, authentication method, and documentation (often primarily in Chinese).

In this guide, we'll show you how to integrate all three models using a single, unified approach — whether you use their native APIs or a gateway like TokenEase.

Option 1: Using TokenEase (Recommended)

The fastest way to get started is through TokenEase, which provides an OpenAI-compatible API endpoint for all major Chinese AI models. This means you can use the standard openai Python library — no new SDKs needed.

Step 1: Get Your API Key

Sign up at tokenease.io/register to get your API key. New users get 1 million free tokens.

Step 2: Install the OpenAI SDK

pip install openai

Step 3: Make Your First API Call

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenease-api-key",
    base_url="https://tokenease.io/v1"
)

# DeepSeek V4 Flash
response = client.chat.completions.create(
    model="deepseek",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    temperature=0.7,
    max_tokens=500
)

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

Step 4: Switch Models Seamlessly

Want to try GLM-5.1 instead? Just change the model name:

# Switch to GLM-5.1 (Zhipu AI)
response = client.chat.completions.create(
    model="glm-5",
    messages=[{"role": "user", "content": "Write a Python function to sort a list"}]
)

# Or Qwen-Plus (Alibaba)
response = client.chat.completions.create(
    model="qwen-plus",
    messages=[{"role": "user", "content": "Translate this to Japanese: Hello world"}]
)

Option 2: Using Native APIs Directly

If you prefer to connect directly to each provider, here's how to set up each one.

DeepSeek API (Direct)

from openai import OpenAI

# DeepSeek's API is OpenAI-compatible
client = OpenAI(
    api_key="your-deepseek-api-key",
    base_url="https://api.deepseek.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Hello!"}]
)

GLM-5.1 API (Zhipu AI)

import zhipuai

zhipuai.api_key = "your-zhipu-api-key"
response = zhipuai.model_api.invoke(
    model="glm-4-plus",
    prompt=[{"role": "user", "content": "Hello!"}]
)

Qwen-Plus API (Alibaba/DashScope)

import dashscope
from dashscope import Generation

dashscope.api_key = "your-dashscope-key"
response = Generation.call(
    model="qwen-plus",
    messages=[{"role": "user", "content": "Hello!"}]
)

Comparison: TokenEase vs Direct APIs

Feature	TokenEase	Direct APIs
Models Available	All in one endpoint	One per provider
SDK Required	OpenAI SDK only	Multiple SDKs
API Keys Needed	1	3+
Pricing	From $0.50/M tokens	Varies by provider
Fallback/Routing	Built-in	Build yourself
Billing	Single invoice (USD)	Multiple currencies

Streaming Responses

Both approaches support streaming for real-time output:

stream = client.chat.completions.create(
    model="deepseek",
    messages=[{"role": "user", "content": "Write a story about AI"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Error Handling & Retries

Production applications need robust error handling:

import time
from openai import OpenAI, APIError, RateLimitError

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

def chat_with_retry(model, messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model=model, messages=messages
            )
            return response.choices[0].message.content
        except RateLimitError:
            time.sleep(2 ** attempt)  # Exponential backoff
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            # Try fallback model
            model = "glm-5" if model == "deepseek" else "deepseek"
    return None

Use Cases & Model Recommendations

Use Case	Recommended Model	Why
Code Generation	DeepSeek V4 Pro	Best coding benchmarks
Chinese NLP	GLM-5.1	Native Chinese training
Fast & Cheap	DeepSeek V4 Flash	$0.50/M tokens
General Tasks	Qwen-Plus	Balanced performance/cost
Multilingual	DeepSeek V4 Flash	Strong on 20+ languages

Conclusion

Integrating Chinese AI models into your Python application has never been easier. With TokenEase, you get a single OpenAI-compatible endpoint that routes to DeepSeek, GLM, and Qwen — with unified billing, automatic fallback, and competitive pricing starting at $0.50 per million tokens.

Whether you're building a chatbot, an AI-powered SaaS, or a data processing pipeline, the approach is the same: install the OpenAI SDK, set your base URL, and start making requests. Switching models is as simple as changing a string parameter.