DeepSeek's Open Source Plan: Analyzing from a Technical and Community Perspective

In the context of the global artificial intelligence (AI) industry witnessing fierce competition between closed-source and open-source models, DeepSeek – an AI startup from China – has created a new wave with its commitment to expanding the open-source code of its models. However, many users and developers remain confused about the company's previous open-source scope. This article will clarify the history of DeepSeek's source code releases, the latest plans, and the implications of these decisions for the global AI ecosystem.

Based on information from the article, here is the timeline of DeepSeek:

Part 1: DeepSeek and Open Source Strategy

1.1. Overview of DeepSeek

DeepSeek was founded in 2023 by Liang Wenfeng, former Director of the High-Flyer investment fund, with the goal of developing open-source large language models (LLMs)[3][7]. Unlike OpenAI or Google, DeepSeek focuses on optimizing model training costs (only $5.6 million for DeepSeek-R1) and leveraging the Mixture-of-Experts (MoE) architecture to reduce computational resources[7][9]. By January 2025, DeepSeek-R1 made waves by achieving performance on par with GPT-4 but at only 5-10% of the cost[14].

1.2. Common Misunderstanding: "DeepSeek Has Open-Sourced Everything"

Many users mistakenly believe that DeepSeek has publicly released the entire system's source code. In fact, prior to February 2025, the company only released a portion of the source code for specific models such as:

DeepSeek-V3 (12/2024): A multilingual model focused on mathematical reasoning and programming[7].
DeepSeek-R1 (1/2025): An MoE model with 671 billion parameters, but only activating 37 billion parameters per task[9][11].

These releases allowed the community to access model implementation code and training datasets, but did not include the entire backend system, API management tools, or security mechanisms[1][4]. This led to serious vulnerabilities, such as the leak of 1 million user data records due to the lack of API authentication mechanisms[1][4].

Part 2: DeepSeek's Open Source Expansion Plans

2.1. Official Announcement from DeepSeek

On February 21, 2025, DeepSeek announced that it would publicly release an additional 5 code repositories in the following week, including:

API management tools: Facilitating the integration of DeepSeek into third-party applications.
Resource monitoring system: Optimizing GPU usage.
MoE training framework: Allowing customization of model architecture[6][13].
Natural Language Processing (NLP) library: Supporting Chinese and English.
Content moderation tools: Complying with Chinese regulations[7][10].

According to a post on platform X, this move aims to "share development progress transparently" and "build a global AI community"[6][13].

2.2. Motivation from Community Pressure and Competitors

DeepSeek's decision was largely driven by two factors:

Pressure from security vulnerabilities: Following a report from Wiz (1/2025), the lack of system source code led to criticism of DeepSeek regarding transparency[1][4].
Competition with Baidu: Baidu's CEO Robin Li announced the open-sourcing of Ernie 4.5 on June 30, 2025, acknowledging learning from DeepSeek[2][5].

Part 3: Explaining the Confusion in the Community

3.1. Causes of Misunderstanding

The term "open source" being conflated: Many users assume that "open source" means the public release of the entire codebase, while DeepSeek only released individual parts[8][10].
Lack of clear promotion: DeepSeek focused marketing on models like R1/V3 without emphasizing the limitations of the source code[7][11].

3.2. Consequences of Misunderstanding

Security risks: Developers integrated DeepSeek into systems without being aware of the API vulnerabilities[4].
Unrealistic expectations: The community expected deep customization capabilities, but in reality, was limited due to the lack of supporting tools[6][13].

Part 4: Impact of the New Plans

4.1. On the AI Community

Promoting interdisciplinary research: The MoE framework opens up opportunities for model development in healthcare, finance[7][9].
Reducing operational costs: The NLP library and API management tools help startups save up to 95% on costs[9][14].

4.2. Potential Challenges

Regulatory conflicts with censorship: DeepSeek's moderation tools may not be suitable for Western markets[7][10].
Competitive risks: Baidu, Alibaba, and Tencent are aggressively integrating DeepSeek into their platforms, diminishing the startup's advantage[2][5].

Conclusion: DeepSeek and the Future of Open Source AI

DeepSeek's open-source plan marks a significant step towards democratizing AI. However, its success depends on balancing transparency and security, as well as building a sustainable contributing community. With "big players" like Baidu following this model, the era of open-source AI could completely change how we approach artificial intelligence technology.

Sources