Different Types of LLMs: What Are They Used For?

Choosing the Right LLM: A Strategic Decision, Not Just a Tech Choice

Today, selecting an LLM is not a technological preference for executives—it’s a direct strategic decision. Using the right model in the wrong context doesn’t just result in a loss of performance; it increases costs, magnifies risks, and damages user experience. At CBOT, working with dozens of large organizations has made one thing clear: Even companies within the same sector can achieve better results with different types of LLMs for similar needs. Because the issue is not choosing “the most powerful model,” but finding the one that fits the job best.

In this article, we break down four commonly confused approaches in the world of large language models: general-purpose models, task-specific solutions, open-source LLMs, and enterprise fine-tuned models. This distinction isn’t just technical—it impacts operations, costs, and user experience. In other words, this article is not for engineers evaluating models, but for decision-makers investing in the right solution.

General-Purpose LLMs: The Power of Flexibility

Models like GPT-4, Claude, and Gemini are general-purpose LLMs trained on extensive datasets. Their biggest advantage is flexibility: they perform well across a wide range of tasks such as text generation, Q&A, summarization, and code writing.

At CBOT, we use these models effectively in projects across various industries, especially when multiple use cases and a broad knowledge base are required. As with any solution, narrowing context and providing guidance is critical. Otherwise, issues with content consistency or controllability may arise.

Where do they work best?
Corporate content creation, knowledge-based assistants, and text-based interaction design. Also a strong starting point for organizations entering the generative AI space.

“General-purpose LLMs adapt quickly to diverse scenarios. A strong foundation paired with smart prompting delivers excellent results.”

Task-Specific LLMs: Clear Goal, Clear Output

Task-specific LLMs are optimized for a particular business function. The goal is to produce fast, consistent, and purpose-driven outputs. For instance, models built solely to answer customer queries, summarize policies, or analyze internal communications fall into this category.

CBOT actively utilizes and deploys these models across customer service and document automation applications. In use cases with direct user interaction, task-specific models stand out with high success rates and low error margins.

Where do they work best?
Chatbots, digital assistants, document classification, and internal process automation. They deliver high efficiency with minimal hardware needs in projects with a well-defined scope.

“Task-specific models get the job done. They don’t just respond to questions—they solve problems.”

Open-Source LLMs: Full Control, Full Responsibility

Models like Meta’s LLaMA, Mistral, and Falcon are open-source. They can be customized and operated entirely in-house, offering significant advantages in data security and model control.

However, this flexibility comes with substantial technical responsibility. Training, optimizing, updating the model, and maintaining the infrastructure fall entirely on the organization. These models require technically mature teams and long-term maintenance strategies.

Where do they work best?
Projects requiring high data confidentiality, critical decision support systems, or internal R&D efforts. But to sustain them long-term, a robust internal structure is essential.

“Open-source models offer freedom—but that freedom comes with a technical commitment.”

Enterprise Fine-Tuned LLMs: Intelligence That Works Like Memory

Enterprise-specific models are trained using an organization’s knowledge base, business processes, and communication style. They don’t just solve isolated tasks—they offer multidimensional solutions. These models reflect the organization’s culture, priorities, and expectations.

At CBOT, the solutions we build in this category stand out especially in projects requiring deep integration. Over time, these models get to “know” the business better, outputs become more precise, and the need for retraining decreases.

Where do they work best?
Internal knowledge management, personalized customer service, proposal generation, content summarization, and multilingual text production. Ideal for automating high-volume operations and standardizing quality.

“Enterprise LLMs don’t just produce the correct output—they produce what’s right for you.”

Conclusion: It’s Not About the Model, It’s About the Strategy

LLM technologies evolve every day. But how that power is positioned matters just as much as the power itself. At CBOT, we believe it’s not enough to ask, “Which model are we using?” The real question should be: “How does this model serve our business strategy?”

General-purpose models are flexible and powerful, task-specific models are fast and reliable, open-source models provide control, and enterprise models create long-term competitive advantage. The key is deciding which of these approaches should be used, in what context, and how.

At CBOT, we make these decisions together, develop models collaboratively, and turn generative AI into a true asset that adds value to your business.