Nowadays you hear about AI transforming businesses multiple times per day. But what is meant by “AI”, anyway?
AI is a very big area, but in practice, especially when applying AI in businesses, this means large language models. And there’s not only one LLM (or “one AI” for that matter) – there are a lot! And all of them come with their individual strengths and characteristics.
In this article we would like to give you a concrete overview for AI model examples currently out there (mid-2025). We can break them down into two main categories: Proprietary AI models and open-source AI models.
Remember: this is not an exhaustive list. There’s new LLMs coming to light every single day, and what’s hot today might not be that relevant anymore in half a year. But knowing the AI model examples we introduce in this text will definitely get you the foundation you need to gain a better understanding and explore further on your own.
What are LLMs and why should your business care?
First, what are LLMs? Answering this question is the first step. When people talk about the AI that writes, chats, and analyzes complex data, they’re referring to ‘Large Language Models’. The simplest way to think about them is as incredibly powerful software engines, each trained on a vast amount of text and data from the internet.
They possess the power to receive instructions in natural language and answer in natural language. We won’t go into the details of how this works in this article, but have a look at YouTube videos like this one in case you wanna know more.
For your business, this is revolutionary. It means you can now leverage software that understands language, context, and intent to automate tasks, generate creative ideas, and uncover insights that were previously out of reach.
There’s a multitude of LLM providers out there, most notably for example OpenAI, Anthropic, Google, Meta, Mistral or Alibaba. Each provider usually has a variety of LLMs out there and continues releasing newer and better ones regularly. There’s even leader boards online that compare the performance of different LLMs (have a look at llm-stats.com or the Open LLM Leaderboard if curious).
Something worth knowing is that these large language models come in two distinct flavors:
- Proprietary AI Models: These are polished, ready-made products created and owned by a single company (like Google or OpenAI). Think of this like subscribing to a powerful cloud service like Salesforce. You pay a fee, get instant access to incredible power, and the provider handles all the complex maintenance. It’s fast, easy, and reliable.
- Open-Source AI Models: These are powerful engines whose blueprints are publicly available. This is where it gets interesting. Because the model is “open,” you have more choice in how you use it, which creates a critical distinction for your business strategy.
Understanding the difference between the two is not too complicated, but quite essential. So we’ll now have a look at AI model examples from both of these two groups.
Proprietary AI model examples
This is the fastest way to get started with world-class AI. These are polished, ready-to-use services from major tech companies like Google, OpenAI, and Anthropic. Think of it like using a top-tier cloud software: you pay for access, and you get state-of-the-art performance without needing a team of engineers to manage servers or hardware.
However, choosing this path involves clear strategic trade-offs. The most critical one being data privacy. When you use these models, your data is sent to the provider’s servers for processing. While these companies have robust security, this is a major consideration for any business with sensitive customer data or proprietary information. In addition to that, these models are kind of a “black box” – their internal workings and the specifics of their training data are not public. You are placing your trust in the provider’s brand, performance, and security policies. This path prioritizes speed and power over transparency and direct control.
The “o” Series and GPT Series (from OpenAI)
OpenAI’s GPT and “o” series are the models that defined the modern AI era. GPT‑3 arrived in 2020 with 175 billion parameters and with the dev community immediately starting building upon it, it quickly became the foundation for countless products. GPT‑4 followed in March 2023, introducing among others GPT‑4o (“Omni”) in late 2024, which added vision and structured output support. In early 2025, OpenAI released o3 and o4‑mini reasoning‑oriented models (April 2025), optimized for longer chain‑of‑thought responses and tool use within ChatGPT. OpenAI is reportedly preparing to launch GPT‑5 in the second half of 2025, integrating its various o‑series and GPT variants into a unified, multitasked system. These models remain proprietary, accessible via ChatGPT and OpenAI’s API (and via select third party platforms like Azure), and fuel everything from Microsoft Copilot to enterprise integrations.
The Gemini Series (from Google)
Google DeepMind’s Gemini series launched in December 2023 and evolved into Gemini 1.5 and current flagship Gemini 2.5, made generally available in early 2025. Gemini 2.5 Pro supports a 1 million token context window (enough to to process a 1,500-page book, with a 2 million token capability coming soon), aligned with multimodal input (text, image, audio, code). A low‑cost Gemini 2.5 Flash‑Lite variant, launched recently, offers fast performance and multimodal support at $0.10 per million input tokens – ideal for scale‑sensitive use cases. Gemini is deeply embedded in Google’s ecosystem via the Gemini app (formerly Bard) and services like Docs, Search, Vertex AI and AI Studio.
The Claude Series (from Anthropic)
Anthropic’s Claude models emphasize trust, safety, and precision. The Claude 3 family launched in March 2024, featuring Claude 3 Haiku, Sonnet, and Opus, with context windows up to ~200K tokens and multimodal capabilities. In November 2024, Claude 3.5 Sonnet was introduced, offering improved reasoning performance, faster speed, and high coding benchmarks, and is available via Claude.ai, Amazon Bedrock, and Anthropic’s API. The Anthropic AI models are also used in Claude Code, a tool increasingly used with the software development community.
The Grok Series (from xAI)
xAI’s Grok series is notable for its real‑time integration with X (formerly Twitter) and fast iteration from Grok‑0 through Grok 4. Grok‑4, officially released July, 2025, supports a 256,000‑token context window, native tool use, live search (via xAI DeepSearch), multimodal inputs, agentic capabilities, and voice interaction like Eve (singing and emotional speech). xAI claims Grok 4 outperforms most grad‑level benchmarks in math and reasoning. Unlike others, Grok continuously pulls real‑time trending info from X, making it especially reactive to live events.
Open-source AI Model examples
This is where you gain strategic control. Open-source AI models are powerful engines whose designs and “weights” (the core of their knowledge) are publicly available. This gives you a critical choice in how you deploy them, moving beyond a simple subscription (as its the case when using proprietary models) to a more strategic decision about your company’s infrastructure and intellectual property.
Deploying open-source AI models
Across all AI model examples for this category, you have basically two options of using open-source AI models. We won’t go into detail but here’s a quick overview.
Option A: Self-Hosting
This means your team installs the open-source model on your own private servers (either on-premise or in a private cloud).
- Why choose this? Main reason: Maximum security and data privacy. Your sensitive data never leaves your infrastructure. You have complete transparency of the model you’re using and can customize it deeply to create a unique competitive advantage that no one else can replicate. This is about building a long-term, proprietary AI capability.
- The catch: It requires significant upfront investment in powerful servers (GPUs) and the in-house technical talent to manage them. (If you want to go down this route, talk to us, at Dentro we offer support with on-premise AI setups)
Option B: Using a third-party API
Many companies now offer hosting for these same open-source models, allowing you to access them via an API, just like a proprietary model.
- Why choose this? Flexibility and cost-efficiency. You can access the specific capabilities of a powerful open-source model (like Llama from Meta) without the headache and cost of managing your own hardware. It frees you from being locked into a single tech giant’s ecosystem and often comes at a lower price point. Also it can make sense in case you are just starting out with your application, as it lets you use a LLM via an API that you could host on your own servers at a later point after your proof of concept phase.
- The catch: While you have more freedom to switch vendors, you are still sending your data to a third party for processing, so the same data privacy considerations apply as with proprietary models.
Here are some leading open-source AI models and why you might choose them.
The Llama Series (from Meta)
LLaMA is Meta’s (Facebook’s parent company) contribution to the open-source AI movement — and arguably the model that kicked it all into high gear. First released in 2023, the LLaMA family has become the gold standard for open-source language models. Meta’s idea was to offer the world powerful AI models that researchers, startups, and developers could freely use and build on. With each version, LLaMA has gotten smarter, faster, and more capable – and today’s LLaMA 3.1 is widely seen as competitive with the best from OpenAI and Google. It can process huge amounts of text, reason well, and power everything from research assistants to chatbots, all without locking developers into expensive proprietary platforms. Meta’s open release strategy also means that LLaMA is available across all the major cloud providers (Microsoft, AWS, and even smaller platforms) making it a popular base model for custom enterprise applications and new startups alike.
The DeepSeek Series
DeepSeek is a rising star out of China, backed by one of the country’s most ambitious AI startups. It burst into the global spotlight in late 2024 with a model that stunned the industry by outperforming some of the big names — and at a fraction of the training cost. But what really sets DeepSeek apart is its specialty: reasoning. These models are built to think — to solve problems, follow complex logic, and even do advanced coding. That makes them ideal for developers, research teams, and financial analysts who need an AI that can go beyond simple chat and actually figure things out. The latest versions (R1 and its updates) even include built-in tool use and memory, meaning you can build agents that plan, retrieve knowledge, or carry out multi-step tasks. It’s quickly becoming a favorite for companies looking to self-host their own private, secure AI stacks.
The Kimi Series
Kimi is the powerhouse model family from Chinese unicorn startup Moonshot AI — and one of the most impressive recent entrants in the open-source scene. Where DeepSeek excels at reasoning, Kimi stands out for comprehension. It’s designed to deeply understand long and complex documents — like contracts, academic papers, or technical manuals — and make sense of them in ways that rival or surpass commercial tools. The newest release, Kimi K2, came out in July 2025 and made headlines for being incredibly capable and fully open. It’s also agentic, which means it can take action, use tools, and even analyze its own mistakes — a major leap forward in what open-source models can do. With support from major cloud platforms and adoption in sectors like legal tech, biotech, and government, Kimi is a name to watch if your business runs on deep knowledge work.
The Qwen Series (from Alibaba)
Qwen is Alibaba’s flagship open-source AI family, and it’s quietly become one of the most globally useful models out there. What makes Qwen special is its multilingual power and its flexibility across formats — it handles text, code, images, even audio and video in newer versions. That makes it ideal for businesses that operate across languages or need AI to work in multiple media types. Qwen is already widely used across Asia and increasingly gaining traction in Europe and the U.S., particularly in developer circles. The latest release, Qwen2.5, came in mid-2025 and includes powerful capabilities built for real-world applications — like customer support, coding assistants, and enterprise search. Alibaba’s decision to release Qwen under a permissive license has helped it spread fast, and it’s now one of the most widely adopted open models for international and enterprise-grade AI.
Conclusion
As you can see from these AI model examples, not only the world of AI is diverse, the world of LLMs is as well. From the plug-and-play power of proprietary models like GPT4 and Gemini to the customizable control of open-source solutions like Llama and DeepSeek, there is an AI engine for every business need. By understanding these different AI model examples, you are better equipped to start your AI journey.
The best way to start is not by picking a model, but by identifying a business problem. Do you need a better analyst, a faster writer, or a more efficient support agent? Once you know the problem you want to solve, you’ll know which type of AI to explore first.
If you would like some further insights or support with picking the right model for your use case, reach out to us by sending an email to office@dentroai.com and we’re happy to help.