DeepSeek User Guide

China's AI Leader, New Benchmark for Open Source Models

Overview

DeepSeek is an open-source large language model series developed by a Chinese AI company, renowned for its exceptional cost-effectiveness, powerful programming capabilities, and innovative reasoning models. In 2026, DeepSeek has become one of the preferred AI tools for developers and enterprises worldwide, excelling in programming, mathematical reasoning, and long-text processing.

This guide will help you fully understand the DeepSeek model series, usage techniques, and best practices.

What is DeepSeek?

Core Features

1. Mixture-of-Experts (MoE) Architecture

Intelligent routing mechanism, activating only partial parameters per token
Significantly reduces inference costs while maintaining high-quality output
Large total parameters but minimal actual computation

2. Ultra-Long Context Window

V4: 1 million tokens (industry-leading)
V3: 128K tokens
Can process entire codebases or ultra-long documents

3. Native Multimodal (V4)

Unified processing of text, images, and video
Support for multimodal content generation

4. Innovative Reasoning Technology

R1 model: Pure reinforcement learning training without manual annotation
Transparent reasoning process with strong interpretability

5. Fully Open Source

MIT/Apache 2.0 license
Fully open model weights
Commercial use supported

DeepSeek Model Family

DeepSeek V4 - Trillion-Parameter Flagship (2026 Latest)

Features:

1 trillion total parameters, ~37B active parameters
1 million tokens context window
Native multimodal support

Core Innovations:

1. Engram Conditional Memory System

Solves long-context retrieval challenges
Precisely locates information within 1 million tokens
97% accuracy on Needle-in-a-Haystack benchmark

2. Manifold-constrained Hyper-connections (mHC)

Optimizes MoE expert collaboration
40% improvement in cross-expert information utilization

3. Hierarchical Sparse Attention

40% reduction in inference costs
Maintains high-quality output

Use Cases:

Large codebase analysis (entire project in one input)
Ultra-long document processing (legal contracts, research reports)
Multimodal applications (image + text hybrid tasks)
System architecture design

Performance Benchmarks:

HumanEval (coding): 98%
SWE-bench Verified: 80%+
GSM8K (math): 96%
Context length: 1 million tokens

DeepSeek V3 - High Cost-Effectiveness General Model

Features:

671B total parameters, 37B active parameters
128K context window
Exceptional cost-effectiveness

Use Cases:

Daily programming work
Data analysis
Technical writing
General reasoning tasks

Performance:

HumanEval: ~90%
GSM8K: ~85%
MMLU: ~88%

Cost-Effectiveness:

Input: ¥1/million tokens
Output: ¥4/million tokens
50x cheaper than GPT-4

DeepSeek R1 - Reasoning-Specific Model

Features:

Pure reinforcement learning training (no SFT)
Transparent reasoning process
Performance close to OpenAI O1

Core Innovations:

Skips supervised fine-tuning, direct RL training
GRPO algorithm reduces training complexity
Automatically generates long chain-of-thought

Training Path:

DeepSeek V3 → R1 Zero (pure RL) → R1 (cold start + RL)

Reward Mechanism:

Accuracy reward: correct answer
Format reward: output in specified format
Language consistency: avoid language mixing

Reasoning Output Format:

<think>
[Reasoning Process]
Model shows complete thinking steps
</think>

<answer>
[Final Answer]
</answer>

Use Cases:

Complex mathematical problems
Logical reasoning tasks
Multi-step problem solving
Research analysis

Performance Benchmarks:

AIME 2024: 79.8% (OpenAI O1: 79.2%)
MATH-500: 97.3%
Codeforces rating: 2029

Usage Recommendations:

Temperature: 0.5-0.7 (recommended 0.6)
Do not use system prompts
For math problems, ask for "step by step"

DeepSeek Coder V2 - Programming-Specific Model

Features:

Support for 338 programming languages
128K context window
Code generation and optimization

Use Cases:

Code generation
Code completion (FIM)
Code review
Bug fixing
Code refactoring

Performance Benchmarks:

HumanEval: Pass@1 ~90%
BigCode-Bench: 2nd place (tied with Claude 3.5 Sonnet)
Aider benchmark: 73%

Programming Language Support:

Mainstream languages: Python, JavaScript, Java, C++, Go, Rust
Frameworks: Django, Spring Boot, React, Vue
Total: 338 languages

Best Practices:

✅ Provide complete project context
✅ Specify tech stack and constraints
✅ Request test writing
✅ Review generated code

Pricing Plans

API Pricing (March 2026)

DeepSeek V4:

Type	Price
Input tokens	¥1-4/million
Output tokens	¥16/million

DeepSeek V3:

Type	Price
Input tokens	¥1/million (cache hit)
Input tokens	¥4/million (cache miss)
Output tokens	¥4/million

DeepSeek R1:

Type	Price
Input tokens	¥1/million (cache hit)
Input tokens	¥4/million (cache miss)
Output tokens	¥16/million

Price Comparison:

Model	Relative Cost
DeepSeek V3	1x (baseline)
GPT-4	50x
Claude Opus	30x

Cost-Effectiveness Advantage:

50x cheaper than GPT-4
30x cheaper than Claude
Similar performance at minimal cost

Core Features in Detail

1. Ultra-Long Context Processing

Feature Description: DeepSeek V4's 1 million tokens context window can handle:

Entire codebases (large projects)
Multiple technical books
Complete legal contract collections
Research paper collections

Usage Example:

I uploaded a frontend project with 100 TypeScript files.
Please:
1. Analyze project architecture
2. Identify potential dependency issues
3. Provide refactoring suggestions
4. Generate architecture documentation

[Upload all files]

Best Practices:

✅ Provide complete context at once
✅ Clarify analysis objectives
✅ Leverage V4's retrieval capabilities
❌ Do not ask in batches

2. Code Generation and Optimization

Feature Description: DeepSeek Coder is optimized for programming:

Multi-language code generation
Code completion (FIM)
Code review
Performance optimization

Usage Example:

Implement a high-performance LRU cache in Python:
- Support concurrent access
- O(1) time complexity
- Thread-safe
- Include unit tests

Output Quality:

✅ PEP8 compliant
✅ Complete comments
✅ Comprehensive error handling
✅ High test coverage

3. Reasoning Task Processing

Feature Description: DeepSeek R1's transparent reasoning:

Shows complete thinking process
Traceable reasoning chain
High accuracy

Usage Example:

Problem: A pond has lotus flowers. Day 1: 1 flower,
Day 2: 2 flowers, Day 3: 4 flowers, doubling each day.
By Day 30, the pond is completely covered.
Question: When is the pond half covered?

[DeepSeek R1 will show reasoning process]

<think>
This is a classic exponential growth problem.
Lotus count doubles each day:
Day 1: 1 flower
Day 2: 2 flowers
Day 3: 4 flowers
...
Day 30: Full pond = 2^29 flowers

Half pond = 2^29 / 2 = 2^28 flowers
This is exactly the count on Day 29.

Answer: Day 29, the pond is half covered.
</think>

<answer>
Day 29
</answer>

4. Multimodal Applications (V4)

Feature Description: DeepSeek V4 natively supports:

Text generation
Image understanding
Video analysis
SVG generation

Usage Example:

Analyze this architecture diagram:
[Upload architecture image]

Please:
1. Explain each module's function
2. Identify potential bottlenecks
3. Provide optimization suggestions

Usage Techniques

Technique 1: Provide Concise Prompts for R1

R1 Model Characteristics:

No need for step-by-step guidance
Automatically generates reasoning process
Concise task description is sufficient

Not Recommended:

Please think step by step:
1. First analyze...
2. Then consider...
3. Then evaluate...

Recommended:

Solve this math problem:
[problem description]

Show your reasoning process.

Technique 2: Leverage Ultra-Long Context

V4's 1 Million Tokens:

Scenario: Analyze entire frontend framework source code

Traditional method:
- Upload file by file
- Ask in multiple sessions
- Lose context

V4 method:
- Upload all files at once
- Global analysis
- Cross-file understanding

Technique 3: Programming Task Best Practices

DO:

✅ Provide complete project background
✅ Specify tech stack
✅ Define code standards
✅ Request test writing

Example:

Project Background: E-commerce platform order service
Tech Stack: Go + gRPC + PostgreSQL

Requirements: Implement order creation API
- Inventory verification
- Discount calculation
- Order number generation
- Transaction guarantee

Please provide:
1. gRPC proto definition
2. Go implementation code
3. Unit tests
4. Performance considerations

Technique 4: Cost Optimization

Leverage Caching:

V3/R1 support caching
Lower price for cache hits
Reuse repeated prompts

Batch Processing:

Combine multiple small tasks
Reduce API calls
Lower total cost

Model Selection:

Simple tasks → V3 (cheap)
Complex reasoning → R1 (strong reasoning)
Large projects → V4 (long context)
Programming tasks → Coder (specialized)

Best Practices

1. Programming Tasks

Scenario: Developing REST API

Prompt Template:

Project Background: [description]
Tech Stack: [framework, language, database]
Core Requirements: [feature list]

Constraints:
- Code standards: [PEP8/Google Style]
- Performance requirements: [response time, concurrency]
- Security requirements: [authentication, authorization]

Please provide:
1. Project structure
2. Core code
3. Test cases
4. Deployment suggestions

2. Data Analysis

Scenario: Analyzing sales data

Prompt:

Data: [upload CSV]
Objectives:
1. Sales trend analysis
2. Product performance comparison
3. Anomaly detection

Output:
- Key insights
- Data visualization suggestions
- Action recommendations

3. Document Processing

Scenario: Analyzing legal contracts

Prompt:

Document: [upload complete contract]

Analysis Requirements:
1. Key terms extraction
2. Risk identification
3. Rights and obligations summary
4. Suggested modifications

4. Research Analysis

Scenario: Literature review

Prompt:

Literature Collection: [upload multiple papers]

Review Requirements:
1. Research theme classification
2. Method comparison
3. Conclusion summary
4. Research gaps

Common Issues and Solutions

Issue 1: R1 Skips Reasoning Process

Symptom: Outputs \n\n, no <think> tags

Solution:

python

# Force add at beginning of prompt
prompt = """Any output must have a thinking process,
output must start with "\n\nHmm"

[Your question]
"""

Issue 2: Unstable Programming Task Quality

Causes:

Insufficient context
Unclear tech stack
Lack of constraints

Solutions:

Provide complete project background
Specify tech stack and versions
Define code standards
Provide example code

Issue 3: Poor Long-Text Processing

Causes:

Not utilizing V4's retrieval capabilities
Unclear analysis objectives

Solutions:

Clearly tell the model what to find
Use V4 instead of V3
Provide complete context at once

Comparison with Other Tools

DeepSeek vs GPT-4

Dimension	DeepSeek V3	GPT-4
Programming	90%	92%
Reasoning	85%	95%
Context Length	128K	128K
Price	¥1-4/million	¥140/million
Open Source	✅	❌

Selection Advice:

Priority on cost-effectiveness → DeepSeek
Rich ecosystem → GPT-4
Open source requirement → DeepSeek

DeepSeek vs Claude

Dimension	DeepSeek V4	Claude Opus 4.6
Context Length	1 million	2 million
Programming	98%	88%
Reasoning	85%	90%
Price	¥1-4/million	¥105/million
Multimodal	Text+Image+Video	Text+Image

Selection Advice:

Programming-intensive → DeepSeek
Ultra-long documents → Claude
Limited budget → DeepSeek

DeepSeek R1 vs OpenAI O1

Dimension	DeepSeek R1	OpenAI O1
Math Reasoning (AIME)	79.8%	79.2%
Code (Codeforces)	2029	2061
Price	¥1-4/million	¥140/million
Open Source	✅	❌

Selection Advice:

Cost-sensitive → R1
Open source need → R1
Slightly higher reasoning → O1

Deployment and Usage

Online Usage

Official Platform:

URL: https://chat.deepseek.com/
Generous free tier
Supports all models

API Calls

Python Example:

python

from openai import OpenAI

# Compatible with OpenAI API
client = OpenAI(
    api_key="your-deepseek-api-key",
    base_url="https://api.deepseek.com"
)

# Call V3
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "user", "content": "Implement quicksort in Python"}
    ]
)

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

Calling R1 Reasoning Model:

python

response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[
        {"role": "user", "content": "Solve this math problem..."}
    ],
    temperature=0.6  # Recommended 0.5-0.7
)

Local Deployment

DeepSeek Coder V2:

bash

# Deploy with vLLM
pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model deepseek-ai/DeepSeek-Coder-V2-Instruct \
    --port 8000

Hardware Requirements:

V3 (671B): 8x A100 80GB
Coder V2 (236B): 4x A100 80GB
Coder V2 Lite (16B): 1x A100 40GB

Advanced Techniques

1. Prompt Optimization

R1 Model Prompts:

For math problems, suggest including:
"Please reason step by step,
and put your final answer within \boxed{}."

V4 Long-Text Prompts:

When analyzing long documents:
1. Explicitly state document length
2. Specify information to extract
3. Request structured output

2. Temperature Parameter Adjustment

Task Type	Recommended Temperature
Programming	0.2-0.4
Math Reasoning	0.5-0.7
Creative Writing	0.7-1.0
Q&A	0.3-0.5

3. Streaming Output

Python Example:

python

stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=[...],
    stream=True
)

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

Security and Privacy

Data Handling

DeepSeek Commitments:

✅ Does not store conversations for training (by default)
✅ Supports private deployment
✅ Compliant with data security standards

Best Practices:

✅ Do not share sensitive information
✅ Use local deployment for confidential data
✅ Regularly clear conversation history

Summary

DeepSeek's Core Advantages

Exceptional Cost-Effectiveness - 50x cheaper than GPT-4
Powerful Programming - HumanEval 98% (V4)
Ultra-Long Context - 1 million tokens (V4)
Innovative Reasoning - R1 pure RL training
Fully Open Source - MIT/Apache 2.0 license

When to Choose DeepSeek

✅ Programming-intensive tasks
✅ Processing large codebases
✅ Limited budget
✅ Need open source models
✅ Chinese content processing

When to Choose Other Tools

Need rich ecosystem → GPT-4
Ultra-long documents (>1M tokens) → Claude
Multimodal (video generation) → Gemini

Next Steps

Take Action Now:

Register DeepSeek account (free)
Test different models (V3/R1/Coder)
Try API calls
Explore ultra-long context features

Deepen Your Learning:

DeepSeek official documentation
GitHub open source repository
Technical papers

References:

DeepSeek Official Documentation (2026)
DeepSeek V4 Technical Report (2026)
DeepSeek R1 Paper (2025)
BigCode-Bench Leaderboard (2026)
Aider Model Evaluation (2025)

DeepSeek User Guide ​

Overview ​

What is DeepSeek? ​

Core Features ​

DeepSeek Model Family ​

DeepSeek V4 - Trillion-Parameter Flagship (2026 Latest) ​

DeepSeek V3 - High Cost-Effectiveness General Model ​

DeepSeek R1 - Reasoning-Specific Model ​

DeepSeek Coder V2 - Programming-Specific Model ​

Pricing Plans ​

API Pricing (March 2026) ​

Core Features in Detail ​

1. Ultra-Long Context Processing ​

2. Code Generation and Optimization ​

3. Reasoning Task Processing ​

4. Multimodal Applications (V4) ​

Usage Techniques ​

Technique 1: Provide Concise Prompts for R1 ​

Technique 2: Leverage Ultra-Long Context ​

Technique 3: Programming Task Best Practices ​

Technique 4: Cost Optimization ​

Best Practices ​

1. Programming Tasks ​

2. Data Analysis ​

3. Document Processing ​

4. Research Analysis ​

Common Issues and Solutions ​

Issue 1: R1 Skips Reasoning Process ​

Issue 2: Unstable Programming Task Quality ​

Issue 3: Poor Long-Text Processing ​

Comparison with Other Tools ​

DeepSeek vs GPT-4 ​

DeepSeek vs Claude ​

DeepSeek R1 vs OpenAI O1 ​

Deployment and Usage ​

Online Usage ​

API Calls ​

Local Deployment ​

Advanced Techniques ​

1. Prompt Optimization ​

2. Temperature Parameter Adjustment ​

3. Streaming Output ​

Security and Privacy ​

Data Handling ​

Summary ​

DeepSeek's Core Advantages ​

When to Choose DeepSeek ​

When to Choose Other Tools ​

Next Steps ​

DeepSeek User Guide

Overview

What is DeepSeek?

Core Features

DeepSeek Model Family

DeepSeek V4 - Trillion-Parameter Flagship (2026 Latest)

DeepSeek V3 - High Cost-Effectiveness General Model

DeepSeek R1 - Reasoning-Specific Model

DeepSeek Coder V2 - Programming-Specific Model

Pricing Plans

API Pricing (March 2026)

Core Features in Detail

1. Ultra-Long Context Processing

2. Code Generation and Optimization

3. Reasoning Task Processing

4. Multimodal Applications (V4)

Usage Techniques

Technique 1: Provide Concise Prompts for R1

Technique 2: Leverage Ultra-Long Context

Technique 3: Programming Task Best Practices

Technique 4: Cost Optimization

Best Practices

1. Programming Tasks

2. Data Analysis

3. Document Processing

4. Research Analysis

Common Issues and Solutions

Issue 1: R1 Skips Reasoning Process

Issue 2: Unstable Programming Task Quality

Issue 3: Poor Long-Text Processing

Comparison with Other Tools

DeepSeek vs GPT-4

DeepSeek vs Claude

DeepSeek R1 vs OpenAI O1

Deployment and Usage

Online Usage

API Calls

Local Deployment

Advanced Techniques

1. Prompt Optimization

2. Temperature Parameter Adjustment

3. Streaming Output

Security and Privacy

Data Handling

Summary

DeepSeek's Core Advantages

When to Choose DeepSeek

When to Choose Other Tools

Next Steps