From Buzzwords to Bottom Lines: Understanding the AI Model Types

There’s an alphabet soup of terms floating around when it comes to artificial intelligence models. There are foundation and frontier models, large and small language models, multimodal models — and the AI model term du jour, reasoning models.

These buzzwords show up in blogs, company announcements, executive speeches, conference panels and quarterly earnings calls, but what do they actually mean? More importantly, why should business users care?

This guide explains key AI model types in plain English and how each affects cost, capability and risk for organizations.

Here are the different types of models often encountered.

Foundation Models: The Base Layer of Generative AI

Foundation models are large, general-purpose AI systems trained on massive datasets such as the entire internet. They serve as the “base model” that can be adapted to perform a wide variety of tasks.

They can include large language models, vision language models, code models and more. They are typically trained to predict the next word in a sentence, the next pixel in an image or the next token in a code sequence.

Foundation models can be frontier models if they bring major new capabilities and push the boundaries of foundation models.

Examples: OpenAI’s GPT family of models; Google’s Gemini; Meta’s Llama; Anthropic’s Claude

Why it matters for business: These models power everything from customer service chatbots to content generation tools. You can either use them as they are, through APIs, or fine-tune (retrain) them on your company’s data to create more specialized applications.

Pros: Versatile, fast to deploy, broadly knowledgeable

Cons: Expensive to run at scale, may hallucinate or generate inaccurate content, are not inherently secure or compliant with regulations

Large Language vs. Small Language Models

Large language models are AI models trained on huge amounts of data to learn language patterns. They power generative AI to create prose, poems, business emails and other language tasks. They are behind today’s most popular chatbots and AI assistants.

Small language models are tinier, cheaper and usually more specialized versions of large language models.

Large language models are often used by AI agents to execute tasks. The agent, which is a system and not a model, is layered on top of the large language model.

Examples: OpenAI’s GPT series; Google’s Gemini; Meta’s Llama; Anthropic’s Claude

Why it matters for business: Large language models can handle several administrative and creative tasks quickly and at scale to save employees hours of work and make business operations more efficient.

Pros: Highly capable in general tasks, can be fine-tuned to specialize in an industry or task

Cons: Expensive to run, prone to hallucinations, may absorb biases from its training data

Reasoning Models: Thinking and Ruminating

Reasoning models are usually fine-tuned versions of large language models designed to think through problems step-by-step. This makes them ideal as a second opinion on decisions, and for answering complex queries or handling more in-depth tasks.

Examples: OpenAI’s Omni, or o series of models; Google’s Gemini 2.5, Meta’s Llama 3.2 series, Anthropic’s Claude 3.7 Sonnet

Why it matters for business: It’s a smarter AI that can dive into more complex tasks, such as explaining a legal contract and its ramifications, not just summarizing the document.

Pros: Greater accuracy, deeper insight, less human oversight needed

Cons: Slower responses, higher compute cost per query

Multimodal Models: Diversity of Inputs

Multimodal models are AI models that can ingest different forms of data (text, video, images and audio).

Examples: OpenAI’s GPT-4o and GPT-4 with Vision; Google’s Gemini family of models; Meta’s Llama 4

Why it matters for business: AI models can now read, analyze and interpret data in many forms, which is practical for businesses using PDFs, Excel sheets, PowerPoints, faxes and other forms of documents.

Pros: Better understanding of context, leading to wider usefulness

Cons: Needs more data and computing power to train and deploy

Open Source vs. Closed or Propriety Models

Open-source AI models generally are free to use, modify and share, and any restrictions vary by the type of license it is using. Their code and weights are publicly available to use.

Closed or proprietary AI models are not free, and they are developed by private companies. Users cannot see inside or modify these models.

Examples:

-Open source: Meta’s Llama family; Google’s Gemma family; several Mistral models; EleutherAI’s GPT-NeoX

-Closed: OpenAI’s GPT-3 and later models; Google’s Gemini; Anthropic’s Claude

Why it matters for business: Closed models are usually more capable and more convenient to use, with a company behind them for support. Open models can be cheaper, and users have more control and customization opportunities. Companies can deploy both types, depending on the use case.

Pros:

-Open source: Free, transparent, customizable, more control

-Closed: More powerful with support from the company that developed it

Cons:

-Open source: More DIY and responsibility, may be less powerful or safe

-Closed: Limited transparency, more expensive, less customizable

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