DeepSeek

DeepSeek Models: Comprehensive Guide & Usage Tutorial (2025)

1. Core DeepSeek Model Matrix

1. Flagship General & Reasoning Series (Current Mainstays)

This series represents DeepSeek's most powerful performance, setting the standard for open-source models globally.

• DeepSeek-R1 Series (Released Jan 2025)
  • Positioning: Specialized in Reinforced Reasoning, featuring "Chain of Thought (CoT)" capabilities similar to OpenAI's o1. It excels in complex logic, mathematical proofs, and deep thinking.
  • DeepSeek-R1: The full-scale model trained on the V3 architecture (671B parameters, 37B active). Its performance rivals GPT-4o and o1-mini, making it the strongest open-source reasoning model available.
  • DeepSeek-R1-Zero: The first-generation pure Reinforcement Learning (RL) model without Supervised Fine-Tuning (SFT). It demonstrates self-evolution capabilities, though its output style is rawer (ideal for research).
  • DeepSeek-R1-Distill (Distilled Versions): To adapt to consumer-grade hardware, DeepSeek "distilled" R1's reasoning capabilities into smaller models based on Qwen2.5 and Llama3:
    • 1.5B / 7B / 8B: Lightweight, suitable for laptops or standard desktops.
    • 14B / 32B: Mid-range. The 32B version is the "Sweet Spot" for value, performing exceptionally well on 24GB VRAM GPUs.
    • 70B: Heavyweight, offering performance close to the full-scale version.
• DeepSeek-V3 (Released Dec 2024)
  • Positioning: The third-generation flagship general-purpose model and the foundation for R1.
  • Features: Utilizes MoE (Mixture of Experts) architecture, supporting a 128K context window. Thanks to FP8 mixed-precision training, generation speed reaches up to 60 TPS (3x faster than V2).
  • Performance: Surpasses other open-source models in knowledge Q&A, long-text processing, and code generation. With extremely low API pricing, it is the top choice for general conversation.

2. Multimodal & Vision Series (Latest Breakthroughs)

• Janus-Pro Series (Released Jan 2025)
  • Positioning: A next-gen unified multimodal model capable of both "Seeing" (Understanding) and "Drawing" (Generation).
  • Features: Decouples visual encoding, ensuring that understanding and generation tasks do not interfere with each other.
  • Janus-Pro-7B: Built on DeepSeek-LLM-7B and SigLIP. It performs excellently in multimodal understanding benchmarks while supporting high-quality text-to-image generation.
  • Janus-Pro-1B: A lightweight version suitable for edge devices.
• DeepSeek-VL2
  • Positioning: A Vision-Language model focused on deep fusion of image and text understanding, ideal for OCR, chart analysis, and visual Q&A.

3. Coding & Professional Series

• DeepSeek-Coder V2
  • Positioning: The benchmark for code intelligence.
  • Features: Supports 338 programming languages. It excels in code generation, completion, bug fixing, and mathematical programming. While V3 is powerful generally, Coder V2 remains highly relevant for specific coding workflows.
• DeepSeekMath & DeepSeek-Prover
  • DeepSeekMath: An early model specialized in mathematics.
  • DeepSeek-Prover V1.5: Combines Reinforcement Learning with Monte Carlo Tree Search (MCTS), dedicated to formal theorem proving.

4. Historical Versions (Archived)

• DeepSeek-V2.5: Released Sept 2024, merging V2-Chat and Coder-V2. A transitional version.
• DeepSeek-V2: Introduced the DeepSeekMoE architecture, optimizing training costs.
• DeepSeek LLM (7B/67B): The early foundational large model series.

2. Usage Tutorial: How to Use DeepSeek

Method 1: Official Web / App (Easiest)

Suitable for general users; no technical background required.

• Web: Visit chat.deepseek.com
  • Register to use the V3 model for free.
  • Click the "DeepThink (R1)" button below the input box to switch to the R1 reasoning model.
• App: Search for "DeepSeek" in your mobile app store.

Method 2: API Integration (Recommended for Developers)

The DeepSeek API is fully compatible with the OpenAI format, allowing for a drop-in replacement.

# Prerequisite: pip install openai
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",  # Apply at platform.deepseek.com
    base_url="https://api.deepseek.com"
)

response = client.chat.completions.create(
    # Model Selection:
    # "deepseek-chat"     -> DeepSeek-V3 (General Chat)
    # "deepseek-reasoner" -> DeepSeek-R1 (Deep Reasoning)
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a professional assistant"},
        {"role": "user", "content": "Write a Snake game in Python"},
    ],
    stream=False
)

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

Method 3: Local Deployment (Privacy & Offline)

1. Download Tool: Visit Ollama.com to download and install.
2. Select Model: Choose the version that fits your GPU VRAM:
   • 4GB - 8GB VRAM: Recommend 7B or 8B versions.
   • 12GB - 24GB VRAM: Recommend 14B or 32B versions.
3. Run Command: Open your terminal (CMD/Terminal) and enter the corresponding command:

# Run 1.5B Version (Ultra-low resource)
ollama run deepseek-r1:1.5b

# Run 8B Version (Mainstream recommendation, fast)
ollama run deepseek-r1:8b

# Run 32B Version (High performance, strong logic)
ollama run deepseek-r1:32b

3. Visual Interface (Optional)

• AI Model Provider: Ollama
• API Host: http://localhost:11434

📌 Summary: Which Model Should You Choose?

Select the model that best fits your specific use case: