DeepSeek Versions Overview and Pros & Cons Analysis

DeepSeek is one of the most notable language model series in the recent AI field. Through multiple version iterations, it has continuously improved its ability to handle various tasks. This article provides a detailed introduction to each version of DeepSeek, covering release dates, key features, advantages, and limitations — serving as a reference for AI enthusiasts and developers.

1. DeepSeek-V1: The Beginning with Strong Coding Capabilities

Release Date:
January 2024

Features:
DeepSeek-V1 is the first version of the DeepSeek series, pre-trained on 2TB of tokenized data. It mainly focuses on natural language processing (NLP) and coding tasks, supporting multiple programming languages and offering strong coding abilities suitable for developers and researchers.

Advantages:

• Powerful coding ability: Supports multiple programming languages, capable of understanding and generating code — ideal for automated code generation and debugging.
• Large context window: Supports up to 128K tokens, enabling handling of complex text understanding and generation tasks.

Disadvantages:

• Limited multimodal capability: Mainly focuses on text processing, lacking image and audio task support.
• Weaker reasoning performance: Although strong in NLP and coding, its logical reasoning ability is weaker than later versions.

2. DeepSeek-V2 Series: Performance Boost and Open-Source Ecosystem

Release Date:
First half of 2024

Features:
The DeepSeek-V2 series features 236 billion parameters — a major upgrade over V1. It offers high performance at low training cost, supports full open-source and free commercial use, greatly promoting AI application adoption.

Advantages:

• High performance and low cost: Training cost is only 1% of GPT-4-Turbo, significantly lowering barriers for research and commercial development.
• Open-source and free for commercial use: Users can freely deploy and modify it, fostering an open and diverse AI ecosystem.

Disadvantages:

• Slow inference speed: Despite large parameters, inference remains slower than newer versions, limiting real-time performance.
• Limited multimodal ability: Still lacks image and audio task handling capabilities.

3. DeepSeek-V2.5 Series: Breakthrough in Math and Web Search

Release Date:
September 2024

The DeepSeek team merged Chat and Coder models in this version, significantly improving general performance and coding reasoning. The Chat model focuses on dialogue systems, while the Coder model is specialized for understanding and generating code. Together, V2.5 shows major improvement over V2 in general-purpose tasks such as Q&A and creative writing.

Comparison Highlights:

• DeepSeek-V2.5 vs ChatGPT-4o-latest: 43% win rate, 8% draw, 49% loss.
• DeepSeek-V2 vs ChatGPT-4o-latest: 31% win rate, 8% draw, 61% loss.
• DeepSeek-V2.5 vs ChatGPT-4o-mini: 66% win rate, 9% draw, 25% loss.
• DeepSeek-V2 vs ChatGPT-4o-mini: 53% win rate, 9% draw, 38% loss.

Features:
DeepSeek-V2.5 introduced major improvements in math reasoning and writing. It also added a web search function, allowing real-time information retrieval and better contextual understanding.

Advantages:

• Improved math and writing: Performs excellently in mathematical reasoning and content creation.
• Web search capability: Allows real-time data access from the internet, enhancing information accuracy and timeliness.

Disadvantages:

• API limitations: Web search is not supported through the public API, limiting its usability.
• Still limited multimodal ability: Despite progress, it still lacks full multimodal integration.

Open Source:
https://huggingface.co/deepseek-ai/DeepSeek-V2.5

4. DeepSeek-R1-Lite: Reasoning Preview Model – Decoding o1 Logic

Release Date:
November 20, 2024

DeepSeek-R1-Lite is a preview version of the R1 reasoning model, designed to rival OpenAI’s o1 model. It excelled in math competitions (AIME) and coding contests (Codeforces), outperforming GPT-4o in several reasoning benchmarks.

Features:
Uses reinforcement learning (RL) with long reasoning chains — up to tens of thousands of tokens. Performs well on math, coding, and logic reasoning tasks.

Advantages:

• Strong reasoning: Outperforms GPT-4o and sometimes o1 in complex logic and math tasks.
• Detailed reasoning transparency: Outputs include full thought processes and validation steps.
• High cost-efficiency: Much lower training cost than comparable models.

Disadvantages:

• Unstable code generation: Performance varies in simple coding tasks.
• Weak factual recall: Struggles with tasks requiring up-to-date external knowledge.
• Language mix issues: Occasionally outputs mixed Chinese-English reasoning text.

5. DeepSeek-V3: Large-Scale Model with Enhanced Reasoning Speed

Release Date:
December 26, 2024

Features:
DeepSeek-V3 is the first Mixture-of-Experts (MoE) model from DeepSeek, featuring 671 billion total parameters (37B active). It delivers high performance across math, reasoning, and coding benchmarks, rivaling GPT-4o and Claude-3.5-Sonnet.

Advantages:

• Exceptional reasoning power: Excels in knowledge-intensive and mathematical tasks.
• Fast inference: Generation speed improved to 60 tokens/sec, tripling V2’s speed.
• Supports local deployment: With FP8 weights open-sourced for privacy and control.

Disadvantages:

• High resource demand: Training requires massive GPU resources.
• Weak multimodal support: Still not optimized for image or audio understanding.

Paper Link:
DeepSeek-V3 Paper

6. DeepSeek-R1: Reinforcement Learning and Research Application (Comparable to OpenAI o1)

Release Date:
January 20, 2025

DeepSeek-R1 is the latest and most advanced model in the series, trained with large-scale reinforcement learning. It is released under the MIT License, allowing full open-source use and model distillation for training smaller custom models.

Advantages:

• Reinforcement-learning-enhanced reasoning: Achieves near parity with OpenAI o1 in math, coding, and logic reasoning.
• Fully open-source: Enables academic research and model fine-tuning via distillation.
• API support: Provides reasoning-chain outputs via model='deepseek-reasoner'.

Disadvantages:

• Limited multimodal support: Still text-focused with no visual or audio capabilities.
• Narrow application focus: Best suited for academic and technical research rather than commercial use.

Paper Link:
DeepSeek-R1 Paper