July 1, 2025: By Jamie Jurgaitis. There are a number of generative AI products available to choose from today (and more to come tomorrow!). Understanding what one to use, though, can be very confusing. Are they all created equal? Which one is “the best?” How do I choose the right one?
At Professional and Executive Education at Rutgers, we can help you decode today’s complex AI landscape so you know which tools to trust, when to use them, and how to apply them responsibly. Here’s a quick, practical snapshot of the top AI models shaping today’s workplace:
🧩 Comparison of the Top 5 AI Models (as of July, 2025)
| Model | Key Strengths | Ideal Use Cases | Watch Outs |
|---|---|---|---|
| 🔵 GPT (OpenAI) | Best-in-class for general-purpose chat, reasoning, creative writing, coding. Handles text, images, and code smoothly. | Brainstorming, business writing, marketing content, coding help. | Closed-source, usage costs can add up. Not tuned to your private data unless you build a secure pipeline. |
| 🟣 Claude (Anthropic) | Focuses on “Constitutional AI” — more thoughtful, polite, and safe. Great at summaries and nuanced conversations. | Legal, HR, policy drafting, sensitive topics where accuracy & tone matter. | May be more restrictive, sometimes “over-refuses” to answer. Closed ecosystem. |
| 🟡 Gemini (Google) | Tightly integrated with Google Docs, Sheets, Gmail. Good for productivity workflows and search. Strong at multimodal tasks. | Workplace productivity, research, collaborative editing, automated notes. | Factual gaps in some tasks; proprietary, so you can’t run it privately. |
| 🟢 LLaMA (Meta) | Open-weight, powerful for local deployment and fine-tuning. Top choice for private AI projects and research. | Privacy-sensitive data, custom internal tools, academic projects. | Needs more setup & tuning. Not fully “open source” — some use restrictions. |
| 🟠 Mistral (Mistral AI) | Small, fast, efficient. Fully open-source with permissive license (Apache 2.0). | Lightweight local chatbots, embedded apps, private deployments. | Smaller context windows, less plug-and-play for big, multi-modal tasks. |
Why Does it Matter for You?
Understanding the differences between AI models is essential because each one has unique capabilities, limitations, and applications. Some models are designed for speed and efficiency, while others prioritize depth of understanding or creativity. Choosing the right model can impact everything from the accuracy of search results to the tone of a customer service chatbot. In fields like healthcare, education, or finance, selecting a model tailored to the task ensures safer and more reliable outcomes. It’s also key to understanding issues like bias, transparency, and data privacy, which can vary significantly between models. As AI becomes more deeply embedded in daily life, being informed empowers users to make smarter, more ethical decisions about the tools they rely on. In short, not all AI is created equal—and knowing the difference helps you use it wisely.
Stay Ahead of the Curve
At Rutgers, you’ll join a community of professionals who aren’t waiting for change — they’re leading it. Rutgers can gives you the edge. Learn from industry experts and Rutgers faculty who cut through the hype. Leave with practical, hands-on skills you can apply immediately. See the full range of AI offerings we have developed to help you stay ahead of the curve.
Don’t let AI happen to you. Make it work for you.
About the author: Jamie Jurgaitis is a Principal Consultant at Deep Lake Insights, which develops actionable decisions for organizations using information that can be derived from the intersection of traditional research methods and new AI tools. Deep Lake Insights is a valued partner of Professional and Executive Education at Rutgers.