Which Characteristic is Common to Closed Source Large Language Models?

• Michael Willson
• April 03, 2025

Some of the most advanced AI models today hold an intriguing secret, they are behind closed doors. Be it ChatGPT, Gemini or even Claude – they shape conversations, generate insights and solve problems, yet no one outside their creators truly knows how they work. These systems process vast amounts of information, but the logic behind their responses remains locked away.

So, what makes them different from open-source models?

Every closed-source large language model shares a defining characteristic: proprietary control. The companies behind them decide who gets access, how they function and what data they use. While this offers security and refined performance, it also raises questions about transparency, fairness and reliance on a single provider.

Many discussions about AI focus on capabilities, speed, accuracy and adaptability. But the bigger conversation? Who controls the technology and what does that mean for those who use it? Understanding this is key to navigating the growing influence of closed-source AI.

Let’s understand the common characteristic to closed source large language models.

What Are Closed Source Large Language Models?

A closed-source large language model (LLM) is an AI system where the company controlling it restricts access to its internal code and training data. Unlike open-source models, which allow developers to study, modify and improve the system, closed-source AI remains locked behind proprietary protections.

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How Do These Models Work?

These models are trained on massive datasets, allowing them to generate human-like responses. They rely on deep learning, complex neural networks and vast amounts of computation to predict and structure language patterns.

However, with closed-source AI, users only interact with the output, they do not see how the model processes information. This control allows companies to protect their investments while maintaining competitive advantages.

Some of the most well-known closed-source LLMs include:

• GPT-4 and other models (OpenAI) – Available through API or ChatGPT subscriptions, but its inner workings remain undisclosed.
• Gemini (Google DeepMind) – Offered via Google Cloud, but the core model stays locked.
• Claude (Anthropic) – Designed for enterprise AI solutions with no public access to its backend code.

These models dominate the AI landscape, but their closed nature leads to various benefits and challenges.

Which Characteristic is Common to Closed Source Large Language Models?

The most significant shared trait among closed-source LLMs is proprietary control. This means the companies behind these models own every aspect of their development, training and deployment. 

Here’s how this impacts users, businesses and the AI industry as a whole.

1. Restricted Access to Source Code

Unlike open-source models, closed-source AI does not allow external developers to examine or modify its algorithms. This means:

• Users can access predefined features but cannot alter or customize the core model.
• Researchers cannot independently audit the system for biases or security risks.
• Companies retain full control over updates, security patches and improvements.

This approach ensures that only the original developers can enhance or change the model. While this increases security, it limits outside contributions. AI models keep evolving. The Certified Artificial Intelligence (AI) Expert™ certification helps you keep up.

2. Proprietary Data Training

Training data plays a critical role in shaping an AI’s responses. Closed-source models rely on privately sourced datasets, meaning:

• The selection process for data remains undisclosed, making it difficult to verify biases.
• Companies can refine their models with high-quality proprietary data.
• Users must trust that ethical standards are maintained during training.

For example, OpenAI’s GPT-4 was trained using a mix of internet data, licensed sources and proprietary datasets. However, the exact contents remain confidential. This secrecy prevents unauthorized replication but raises concerns about fairness and accountability.

3. Commercial Monetization and Vendor Lock-In

Since closed-source LLMs require significant investment, companies monetize access through subscriptions, API licenses and enterprise solutions. This leads to:

• Recurring costs for businesses using these models in their products.
• Limited flexibility, as switching to another provider requires adapting to a new system.
• Dependence on proprietary technology, which may become costly in the long run.

For instance, Google’s Gemini integrates seamlessly with Google Cloud services. While this provides convenience, it also locks users into a single ecosystem, making migration difficult.

4. Security and Compliance Advantages

One benefit of closed-source AI is stronger security measures and compliance with regulatory standards. Since companies control every aspect, they can:

• Prevent unauthorized modifications that might introduce security vulnerabilities.
• Ensure compliance with data privacy laws, such as GDPR and HIPAA.
• Protect against external tampering, making them suitable for sensitive applications.

For example, IBM’s Watson AI is widely used in healthcare and finance, where strict security measures are essential. Keeping the model closed ensures consistent compliance with industry regulations.

5. Limited Transparency and Ethical Concerns

The biggest criticism of closed-source LLMs is the lack of transparency in how they operate. Since users cannot access their code, several issues arise:

• Bias detection is difficult, as external audits are not possible.
• Accountability becomes a concern, as companies control how information is processed.
• Misinformation risks increase, as users cannot verify the reliability of sources.

For example, OpenAI provides safety measures but does not disclose its full dataset. This means users must trust the company’s assurances regarding bias and misinformation without independent verification.

How Do Closed-Source LLMs Compare to Open-Source Models?

To better understand their impact, let’s compare closed-source and open-source AI models:

Feature	Closed-Source AI (GPT-4, Gemini, Claude)	Open-Source AI (LLaMA, Grok, DeepSeek)
Code Access	Restricted to company	Open for developers
Training Data Transparency	Private	Public or partially disclosed
Customization	Limited	Fully customizable
Security	Strict, controlled by developers	Dependent on community contributions
Monetization	Subscription-based or enterprise pricing	Mostly free with optional paid support

While open-source models offer greater transparency and flexibility, closed-source AI provides better security and refined performance. The choice depends on the needs of businesses and users.

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Should Businesses Choose Closed-Source AI?

For companies considering AI integration, closed-source LLMs provide:

• Advanced features without development costs
• Secure and compliant solutions for regulated industries
• Enterprise support for seamless integration

However, they also come with:

• High costs for licensing and API usage
• Limited flexibility, making switching providers difficult
• Lack of transparency, raising ethical concerns

Final Thoughts

The most common characteristic of closed-source large language models is their proprietary nature. This allows companies to control who accesses them, how they function and how they evolve. While this ensures security and high performance, it also limits transparency and raises ethical questions. As AI continues to shape industries, the debate between open-source vs. closed-source AI will grow.