How to use DeepSeek: A beginner’s Step-by-Step guide

DeepSeek can be used in two primary ways: via the official Web Platform for chat-based interaction or through the DeepSeek API for integration into applications. To start, your essential first step for API access is to obtain an API Key from the DeepSeek Platform (or a compatible service like OpenRouter), as the API is compatible with the OpenAI SDK structure.

Whether you’re a developer looking to integrate powerful AI capabilities into your application or a casual user wanting to explore advanced language models, this guide will walk you through everything you need to know.

Quick Start: Choose Your Method

For Chat/Web Use:

• Navigate to chat.deepseek.com and log in to start a conversation immediately.

For Application Integration (API):

1. Get an API Key from the DeepSeek Platform or a key partner.
2. Set the Base URL: https://api.deepseek.com
3. Use the OpenAI SDK (Python/Node.js) to make your first chat.completions.create request.

---

Method 1: Using the DeepSeek Web Interface (Easiest Start)

The DeepSeek web interface is the simplest way to experience the power of DeepSeek’s language models without any coding knowledge. This method is perfect for quick testing, casual conversations, or exploring what DeepSeek can do.

Step 1: Access the Platform

Visit chat.deepseek.com directly in your web browser. The interface is clean, intuitive, and designed for immediate use.

Step 2: Create Your Account

You have multiple sign-up options for convenience:

• Email registration: Use your email address and create a password
• Google sign-in: Quick authentication with your Google account
• Phone number: Register using your mobile number

The registration process takes less than a minute and requires basic verification.

Step 3: Start Your First Conversation

Once logged in, you’ll see a clean chat interface. Here’s how to make the most of it:

1. Type your prompt in the text box at the bottom of the screen
2. Press Enter or click Send to submit your request
3. Review the response as DeepSeek generates it in real-time

Example first prompts to try:

• “Write a Python function to calculate fibonacci numbers”
• “Explain quantum computing in simple terms”
• “Help me debug this JavaScript code: [paste your code]”

Best For:

• Non-developers exploring AI capabilities
• Quick prototyping and testing ideas
• Learning about DeepSeek’s strengths in coding and reasoning
• Users who prefer a visual, no-code interface

---

Method 2: Integrating with the DeepSeek API (For Developers)

The DeepSeek API opens up powerful possibilities for integrating advanced AI capabilities directly into your applications, scripts, and workflows. Thanks to its OpenAI-compatible structure, migration and integration are straightforward.

Step 1: Obtain Your DeepSeek API Key

Where to Get Your Key:

1. Go to the DeepSeek Platform (platform.deepseek.com)
2. Sign up or log into your account
3. Navigate to the API Keys section in your dashboard
4. Click “Create New Key” and give it a descriptive name
5. Copy and save your key immediately — you won’t be able to see it again

🔒 Security Warning: Never expose your API key in public repositories or client-side code. Store it securely using environment variables or secrets management tools. Treat your API key like a password — anyone with access can make requests on your account.

Alternative: Using OpenRouter
OpenRouter provides access to DeepSeek models with a single API key that also works with other LLM providers. Visit openrouter.ai to get started if you prefer a unified API gateway.

Step 2: Set Up Your Development Environment

Prerequisites:

• Python 3.7+ or Node.js 14+ installed on your system
• A text editor or IDE (VS Code, PyCharm, etc.)
• Basic familiarity with making API calls

Install the OpenAI SDK:

For Python:

pip install openai

For Node.js:

npm install openai

Set Your API Key as an Environment Variable:

Linux/Mac:

export DEEPSEEK_API_KEY='your-api-key-here'

Windows (Command Prompt):

set DEEPSEEK_API_KEY=your-api-key-here

Windows (PowerShell):

$env:DEEPSEEK_API_KEY='your-api-key-here'

Step 3: Making Your First API Request

Now comes the exciting part — actually calling the DeepSeek API and getting a response!

Python Example:

from openai import OpenAI

# Initialize the client with DeepSeek's base URL
client = OpenAI(
    api_key="your-deepseek-api-key",
    base_url="https://api.deepseek.com"
)

# Make your first request
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function to reverse a string."}
    ],
    stream=False
)

# Print the response
print(response.choices[0].message.content)

cURL Example:

curl https://api.deepseek.com/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "deepseek-chat",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Node.js Example:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.DEEPSEEK_API_KEY,
  baseURL: 'https://api.deepseek.com'
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'deepseek-chat',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'Explain async/await in JavaScript.' }
    ]
  });

  console.log(completion.choices[0].message.content);
}

main();

Key API Parameters Explained:

• base_url: Always set to https://api.deepseek.com for DeepSeek API
• api_key: Your secret authentication token
• model: The specific DeepSeek model you want to use (see next section)
• messages: Array of conversation history with roles (system, user, assistant)
• stream: Set to true for real-time token streaming, false for complete responses

Step 4: Understanding DeepSeek Models

DeepSeek offers different models optimized for specific use cases. Choosing the right model impacts both performance and cost.

Model Name	Best For	Key Strengths	Cost (Approx.)
deepseek-chat (DeepSeek-V3)	General conversation, coding, text generation	Fast responses, balanced performance, cost-effective	Lower cost per token
deepseek-reasoner (DeepSeek-R1)	Complex reasoning, mathematics, advanced problem-solving	Chain-of-thought reasoning, exceptional logic	Higher cost per token

When to use deepseek-chat (V3):

• General chatbot applications
• Code generation and debugging
• Content creation and summarization
• Quick responses needed
• Cost-sensitive projects

When to use deepseek-reasoner (R1):

• Mathematical problem-solving
• Complex logical reasoning tasks
• Multi-step problem decomposition
• Research and analysis
• When accuracy matters more than speed

---

Advanced DeepSeek API Features

Multi-Round Conversations

To maintain context across multiple exchanges, simply append new messages to the messages array:

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a function to sort an array."},
]

# First response
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

# Add assistant's response to history
messages.append({
    "role": "assistant", 
    "content": response.choices[0].message.content
})

# Continue the conversation
messages.append({
    "role": "user", 
    "content": "Now modify it to sort in descending order."
})

# Second response with full context
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

Key Parameters for Fine-Tuning

Temperature (0.0 – 2.0):

• Lower values (0.0 – 0.3): More deterministic, focused responses. Perfect for code generation or factual answers.
• Higher values (0.7 – 1.0): More creative, varied outputs. Better for brainstorming or creative writing.

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages,
    temperature=0.2  # More focused responses
)

Stream (True/False):

• stream=True: Receive tokens as they’re generated (like ChatGPT’s typing effect)
• stream=False: Wait for the complete response

# Streaming example
stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages,
    stream=True
)

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

OpenAI Compatibility Advantage

DeepSeek’s API structure mirrors OpenAI’s API exactly, making migration incredibly simple. If you have existing code using OpenAI, you only need to change:

1. The base_url to https://api.deepseek.com
2. Your API key

Everything else — your code structure, error handling, and parameters — remains identical.

Cost Management Tips

💡 Pro Tip: Monitor your token usage to control costs. Each API response includes usage information showing prompt tokens, completion tokens, and total tokens consumed.

print(f"Tokens used: {response.usage.total_tokens}")
print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Completion tokens: {response.usage.completion_tokens}")

Best practices for cost optimization:

• Use deepseek-chat for routine tasks
• Reserve deepseek-reasoner for complex problems requiring deep reasoning
• Implement token limits using the max_tokens parameter
• Cache common responses when possible
• Trim conversation history to essential context only

---

Running DeepSeek Locally (Advanced Users)

For users who need complete privacy, offline access, or want to avoid API costs, DeepSeek offers open-source models that can run on your own hardware.

Available Tools:

• Ollama: Simplest method for running LLMs locally with a single command
• LM Studio: User-friendly GUI application for Windows, Mac, and Linux
• Hugging Face Transformers: Maximum flexibility for custom implementations

Getting Started:

1. Visit the official DeepSeek GitHub repository or Hugging Face model hub
2. Download the model weights (DeepSeek-V3 or DeepSeek-R1 variants)
3. Follow the specific setup instructions for your chosen tool

Hardware Requirements:

• Minimum 16GB RAM (32GB+ recommended)
• GPU with 8GB+ VRAM for optimal performance (CPU inference is possible but slower)
• 50GB+ free disk space for model storage

Note: Local deployment requires technical expertise and significant computational resources. For most users, the cloud API offers the best balance of convenience and performance.

---

Troubleshooting Common Issues

“Authentication Error” or “Invalid API Key”:

• Verify your API key is correct with no extra spaces
• Check that you’ve set the correct base_url
• Ensure your API key hasn’t been revoked in the dashboard

“Rate Limit Exceeded”:

• You’ve made too many requests in a short time
• Wait a few moments before retrying
• Consider implementing exponential backoff in your code

Slow Response Times:

• deepseek-reasoner models take longer due to their reasoning process
• Consider using deepseek-chat for faster responses
• Check your internet connection

Empty or Incomplete Responses:

• Your prompt may be too vague — be more specific
• Check if you’ve hit the token limit
• Increase max_tokens parameter if needed

---

Next Steps

Now that you know how to use DeepSeek, here are some ideas to explore:

• Build a chatbot for your website using the API
• Automate code reviews by integrating DeepSeek into your CI/CD pipeline
• Create a research assistant that helps analyze complex documents
• Experiment with different prompts to discover DeepSeek’s reasoning capabilities
• Compare models by running the same prompt through both deepseek-chat and deepseek-reasoner

DeepSeek’s combination of powerful reasoning, cost-effectiveness, and OpenAI compatibility makes it an excellent choice for developers and researchers. Whether you’re using the simple web interface or building complex applications with the API, you now have all the tools you need to get started.

Ready to dive deeper? Check out the official DeepSeek documentation for advanced features, model specifications, and best practices.