Open vs. Closed Models: What's the Difference?

As you look at Large Language Models, you'll encounter another significant distinction: whether a model is "open" or "closed." This difference fundamentally shapes how you can access, use, and understand these powerful tools. It's not just a technical detail; it has practical implications for cost, customization, and transparency. Let's break down what these terms mean.

Closed Models: Polished but Proprietary

Think of closed models like commercial software applications. They are typically developed, owned, and managed by a specific company.

• What they are: These are proprietary models where the internal workings, specific training data, and often even the full model architecture are kept confidential by the company that created them. You usually interact with these models through controlled interfaces, most commonly Application Programming Interfaces (APIs) or web-based chat applications provided by the vendor.
• Access: Access is usually managed and often involves usage fees based on how much you use the model (e.g., cost per token processed). You don't get direct access to the model's core files or weights.
• Examples: Well-known examples include models like OpenAI's GPT-4 (powering services like ChatGPT Plus and the API), Anthropic's Claude series, and Google's Gemini models (accessible via their API and products).
• Characteristics:
  • High Capability: Often represent the state-of-the-art in performance due to massive investment in training data and computational resources.
  • Ease of Use: Designed for relatively easy integration via APIs or polished web interfaces.
  • Limited Transparency: You generally don't know the exact data they were trained on or the specific details of their architecture. This can make it harder to understand potential biases or limitations.
  • Less Customization: You typically cannot modify the underlying model itself. Customization is often limited to techniques like prompting or perhaps fine-tuning offered as a separate service by the provider.
  • Vendor Dependence: Relying heavily on a specific closed model can make it harder to switch providers later.

Closed models are often a good starting point if you prioritize ease of access, cutting-edge performance straight out of the box, and don't need deep customization or transparency into the model's internals.

Open Models: Accessible and Adaptable

In contrast, "open" models (sometimes referred to as open-weight or open-source, though licensing details can vary) offer greater access and transparency.

• What they are: With open models, main components like the model's architecture details and, most importantly, its trained parameters (weights) are publicly released. Sometimes, details about the training data or even the data itself are also shared. This allows anyone with the necessary skills and computational resources to examine, modify, and run the model themselves.
• Access: The model weights can typically be downloaded. You can run these models on your own hardware (if powerful enough) or on cloud computing platforms. Many communities and companies build interfaces and services around popular open models.
• Examples: Prominent examples include Meta's Llama series (like Llama 3), Mistral AI's models (like Mistral 7B and Mixtral), and models from communities like EleutherAI or institutions like TII (e.g., Falcon).
• Characteristics:
  • Transparency: Researchers and developers can inspect the model's architecture and weights, fostering a better understanding of its behavior and potential biases.
  • Customization: Users can fine-tune these models on their own specific data to improve performance for particular tasks or domains.
  • Control: Running the model locally or on your own infrastructure gives you complete control over its usage and data privacy.
  • Community Innovation: Openness often leads to rapid innovation, with the community building tools, improvements, and new applications around the models.
  • Resource Requirements: Running larger open models can require significant computational power (memory, GPUs). Getting started might involve more technical setup compared to using a closed model's API.
  • Performance Variability: While many open models are highly capable, their out-of-the-box performance might sometimes lag behind the largest, most resource-intensive closed models, although this gap is constantly narrowing.

Open models are attractive if you value transparency, need to customize a model for specific needs, want greater control over deployment and data, or wish to participate in the broader research and development community.

Comparing Access and Control

The core difference lies in accessibility and control over the model itself. Closed models offer a service, while open models provide the underlying components.

A simplified view comparing the access pathways for closed versus open models. Closed models typically involve interacting through a provider's interface, while open models allow direct access to the model components for local use or modification.

Why This Matters For You

As a beginner, understanding this distinction helps you navigate the choices available:

• Experimentation: Many closed models offer free tiers or trials via web interfaces (like the free version of ChatGPT or Claude), making them easy to experiment with initially. Open models might require using platforms like Hugging Face or setting up local environments, which involves a bit more setup but offers deeper learning opportunities.
• Cost: Using closed model APIs usually incurs costs based on usage. Running open models can be free if you have suitable hardware, but cloud computing costs can apply if you use hosted infrastructure.
• Future Projects: If you envision building applications that require significant customization or where data privacy is a major concern, exploring open models might be more suitable in the long run. If you need top-tier performance with minimal setup for common tasks, a closed model API might be the faster route.

Both open and closed models play important roles in the AI ecosystem. Knowing the difference helps you choose the right type of model based on your goals, resources, and desired level of control and transparency. As you progress, you might find yourself using both types for different purposes.