Understanding Frontier Models: The Ultimate Guide to Modern LLMs (GPT, Claude, Gemini, and More)

In today’s fast-paced AI landscape, the term frontier models often comes up — but what exactly does it mean? Frontier models refer to the most advanced Large Language Models (LLMs) that push the boundaries of what’s currently possible. These include both closed-source super-scalers like GPT and Claude, and powerful open-source models such as Llama and Mistral.

What Are Frontier Models?

Frontier models are the largest and most capable LLMs available today. They represent the leading edge of artificial intelligence research and deployment. Often, when people use this term, they’re referring to paid, closed-source models like OpenAI’s GPT or Anthropic’s Claude. However, the term can also include open-source giants like Llama from Meta or Mistral from France.

The Big Names in Closed-Source Frontier Models

• GPT (OpenAI): The model that started the AI revolution when ChatGPT launched in late 2022. It remains the industry benchmark for conversational intelligence.
• Claude (Anthropic): A favorite among data scientists, Claude often rivals GPT in capability and reasoning. Many consider it the most balanced model on the current leaderboards.
• Gemini (Google): Google’s flagship frontier model, which complements its open-source sibling, Gemma.
• Command R (Cohere): Developed by Canadian AI company Cohere, this model focuses on reasoning and retrieval-based performance.
• Perplexity: A hybrid between a search engine and an AI model, Perplexity integrates model responses with live web data for dynamic answers.

Leading Open-Source Models

• Llama (Meta): Meta set the open-source AI world in motion by releasing Llama, enabling developers everywhere to experiment with world-class LLMs.
• Mistral: A French-developed model that uses a Mixture of Experts approach, combining several smaller models to achieve outstanding performance.
• Qwen (Alibaba Cloud): A powerful and efficient model from Alibaba, known for multilingual performance and compact optimization.
• Gemma (Google): The smaller, open-source cousin of Gemini — designed for developers who want lightweight yet capable models.
• Phi (Microsoft): Microsoft’s compact and efficient model, great for edge and on-device AI tasks.

Three Main Ways to Use LLMs

Understanding the different ways to use models is crucial for any AI engineer. There are three core methods:

1. Chat Interfaces

This is the most familiar approach — web-based tools like ChatGPT or Claude.ai. You simply type your query, and the model responds in real time. Everything runs in the cloud, and the user interacts through a browser-based front end. These often come with free tiers or monthly subscriptions.

2. Cloud APIs

Cloud APIs let you connect to the same powerful models — but via code, not a chat window. For example, calling openai.chat.completions.create() in Python connects directly to GPT’s API. You pay per request rather than a flat monthly fee. Tools like LangChain act as abstraction layers that unify these APIs, letting developers easily switch between providers.

3. Managed AI Cloud Services

Companies like Amazon, Google, and Microsoft offer fully managed AI services that host LLMs on their clouds:

• Amazon Bedrock – Amazon’s multi-model AI platform.
• Google Vertex AI – Offers model training and deployment tools under one roof.
• Microsoft Azure ML – Integrated AI development environment for enterprise-scale deployments.

These services allow you to call both open-source and closed-source models via unified APIs — simplifying enterprise integration.

4. Running Models Locally

For developers who prefer control and privacy, running models locally is a powerful option. There are two main ways:

• Using Hugging Face: You can download both the model weights and code, often written in PyTorch, and run inference manually. This gives you deep control, though you’ll likely need a high-performance machine or a cloud GPU (via Google Colab, for example).
• Using Ollama: A simplified tool that runs optimized versions of LLMs locally using C++ backends like llama.cpp. Ollama is ideal for testing and experimenting with models directly on your computer without deep technical setup.

Understanding the LLM Landscape

In summary, there are three core ways to interact with large language models:

1. Chat Interfaces (ChatGPT, Claude, etc.)
2. Cloud APIs and managed AI services
3. Local deployment with tools like Hugging Face or Ollama

Each approach has its advantages — cloud APIs are great for production scalability, while local deployments offer privacy and hands-on control. Understanding when and how to use each one is a vital skill for every modern AI developer.

Next Steps: Practical Application

Now that you understand the landscape, it’s time to get hands-on. The upcoming exercise will involve Ollama — a powerful local runtime for running LLMs right on your computer. This will help you build confidence in managing models directly and understanding their performance in real-time.

Let’s dive into the next step and explore how to put this knowledge into action.