David has been working on his market analysis for hours now. A jungle of Excel tables, figures, and graphics… The presentation has to be ready in 30 minutes. His head is spinning. Two tables away, Lea is typing away on her laptop as if she were just writing her grocery list. She leans back and grins. “Done.”
David raises his eyebrows. “Done?! What do you mean, done? Let me see!” He looks at her screen in disbelief. Perfect diagrams. Crystal clear insights. An analysis that would take him hours.
“How did you do this so quickly?”
Lea turns her chair slightly to the side. “DeepSeek V3. That thing can sift through huge amounts of data faster than we can say ‘deadline’.” David’s stomach tightens. He’d been using GPT-4 specifically. OpenAI’s big thing. Everyone’s using that, right?
Maybe he should take a closer look at what AI models are out there. And what they’re actually good for.
Why Is this Important for Companies?
For companies that are serious about digital transformation, the targeted use of relevant AI models is essential. Saving time by automating processes is one thing, but why not have a strategy or analysis generated? Writing, programming, drawing, letting suggestions inspire you, or getting feedback – AI models are versatile if you know which one is best suited to the task at hand.
With so many vendors and models, it’s not easy to keep up. In this article, we will show you which AI models you should be aware of.
GPT-4 (OpenAI)
Let’s start with OpenAI’s best-known model, GPT-4, which has gained significant traction since its launch in November 2022. In December 2024, OpenAI reported that ChatGPT had 300 million weekly active users worldwide[1].
More than half of German citizens (53 percent) have already used generative AI. This is the result of a representative Forsa survey of 1,001 people over the age of 16 commissioned by the TÜV association[2].
In the field of AI tools, ChatGPT has a dominant market share of around 62.5 percent[3].
OpenAI also offers the ability to create custom GPTs and assistants. Custom GPTs are user-defined variants of GPT-4 that can be customized to meet specific needs. Wizards extend the use of GPT by combining AI models with custom tools and files to simplify complex tasks such as data analysis or automation.
The model is based on a powerful transformer architecture and is trained to produce human-like text. It can be used for creative copywriting in marketing and corporate communications, such as presentations, reports and summaries, as well as for technical writing and search engine optimization. In the current version, adjustments can be made on a canvas directly in the generated text.
In addition to text processing, OpenAI has also integrated its own image generation AI DALL-E 3 into ChatGPT since mid-2003. Since then, images can be generated directly in the conversation by entering a description.
OpenAI offers ChatGPT as a cloud service. The models run on OpenAI’s servers, and users can access them through the website, apps, or the API. Companies can use the API to integrate ChatGPT into their own applications (e.g. chatbots, support systems, automation).
Gemini (Google DeepMind)
A closer look at Google DeepMind’s Gemini shows: Although no specific user numbers have been released, Google’s goal is to have 500 million users worldwide for Gemini by the end of 2025. This would be 200 million more than there are currently registered on GPT every week. This ambitious goal underscores Google’s ambition to become a leader in generative AI[4].
This model is characterized by its multimodality, meaning that it can process and generate both text and images. This makes it ideal for research, design, and development organizations that need AI for visual and language-based analysis. Its strength lies in interpreting and synthesizing different types of data, which distinguishes it from GPT-4, which focuses primarily on language processing.
Gemini is delivered as a cloud-based service and is accessible through the Gemini app and Google Search. Developers can access the model through the Gemini API in Google AI Studio and Vertex AI. The latest version, Gemini 2.0 Flash, will be released to developers in December 2024, followed by general availability in February 2025. In addition, the most cost-effective model “Flash-Lite” was introduced to meet the different needs of users.
Deep Seek V3 (Deep Seek)
The recently released open source model DeepSeek V3 from the Chinese company DeepSeek has caused a sensation. Shortly after its release, the DeepSeek application became the most downloaded free application in the App Store, overtaking its established competitor ChatGPT.
This model is particularly impressive in software development: on the Codeforces platform, which organizes programming competitions, it proved to be more powerful in direct comparison with competing models such as GPT-4. DeepSeek V3 also came out on top in the Aider Polyglot benchmark test. This test measures, among other things, how well a model is able to write new code and integrate it seamlessly into existing projects[5].
DeepSeek V3 is available as an open source model and offers both a basic and a chat version. A local instance allows users to run the model directly on their own servers. This ensures greater privacy, as no data needs to be sent to external servers. It also gives them more control over usage and the ability to customize the model. It is recommended to run DeepSeek in Docker containers to simplify installation and administration.
Claude (Anthropic)
Our next stop is Claude, developed by Anthropic. Claude is based on the concept of “constitutional AI”, where safety guidelines are built into the model. As a result, it pays special attention to security issues and is trained to provide ethical and secure answers.
This is particularly useful for companies that prioritize the responsible use of AI. Making it particularly suitable for legal texts, sensitive data processing and compliance management, as well as for text and code generation. While competitors such as Gemini and GPT-4 offer a broader range of applications, Claude stands out for its reliability and security mechanisms.
Claude is delivered as a cloud-based service and is accessible across multiple platforms. Users can access it through the claude.ai website and mobile apps for iOS and Android. For developers, Anthropic offers an API to integrate Claude into their own applications. Claude is also available through Amazon’s Bedrock platform, enabling easy integration into AWS-powered applications.
Mistral (Mistral AI)
Mistral AI is a French AI startup founded in May 2023 that specializes in the development of powerful open source language models. Well-known models are Mistral 7B and Mixtral 8x22B. They are designed to work efficiently even in resource-constrained environments, and are particularly suitable for real-time applications or scenarios where computational capacity is limited. While DeepSeek V3 or GPT-4 are designed to handle large amounts of data, Mistral offers a leaner alternative with highly optimized performance.
Mistral AI offers its models as open source solutions under the Apache 2.0 license. The models can be hosted locally or deployed via cloud platforms such as Amazon Bedrock. Through its partnership with AWS, Mistral AI uses the AI-optimized AWS Trainium and AWS Inferentia instances to develop and deploy its base models[6].
Llama 2 (Meta)
Our final stop brings us to Llama 2, the evolution of Meta’s original Llama model. Partnerships with major cloud providers such as Microsoft and Amazon have expanded the model’s reach. It is designed to provide developers and researchers with public access to advanced AI technology. It is freely available and can be flexibly integrated into your own applications. Especially for AI beginners who want to develop custom AI applications, this free model can be a good place to start.
Llama 2 can be used for text generation, for example to create marketing content. It can also be used in software development to generate code snippets. In addition, the model is highly trained in language understanding and is often used to analyze and process natural language. Llama 2 provides powerful text processing tools such as conversation and context management, and outperforms Chat GPT in specialized applications such as customer support or education[7]. Thanks to its open source license, Llama 2 can be run on local machines, allowing for complete control and customization. The model is also available on several cloud platforms. It is available in the Azure AI Model Catalog, which allows easy integration into Azure-based applications, users can deploy Llama 2 models via Amazon Bedrock, and deployment is also possible via Vertex AI in the Google Cloud.
Data Protection and Cyber Security in AI Models
But what happens to the data and where is it stored? Privacy and cybersecurity play a critical role in the use of AI models. Many of the leading AI models, such as GPT-4, Claude or Gemini, process large amounts of sensitive data. It is therefore important to pay attention to what information is fed into the models and how it is used.
Another important issue is how vendors handle the data. With ChatGPT, data is usually used to improve the model, but OpenAI offers the option not to use your data for training purposes in the free subscription by unchecking a corresponding option in the settings. With a business subscription (e.g. ChatGPT Plus), the data is not used for model training. ChatGPT can also be hosted on the Azure cloud and more securely integrated into applications via an API.
Google’s Gemini follows similar practices, using data for training purposes in most cases, but companies can also choose options where their data is not used for model training.
Some vendors, such as Anthropic with Claude, place a special emphasis on the safety and ethical use of AI. Open source models, such as Llama 2 or Mistral, allow companies to implement their own privacy policies by running the AI models on their own servers.
Although Deep Seek’s servers are located in China, Deep Seek V3 can also be hosted locally. The advantage of this is independence from external servers and better control over privacy policies. By hosting on their own servers, companies that process sensitive data can ensure that information does not inadvertently flow through third-party vendors. However, this autonomy comes at the cost of higher costs and increased management overhead.
Ultimately, the choice between a cloud or on-premises solution is a trade-off between flexibility and control – a local hosting option would suit companies that prioritize data protection and security.
As a general rule, it is always important to consider what data protection is required before implementation.
Note
Even the most modern AI models are not infallible. So-called AI hallucinations, i.e. the generation of false, invented information, pose a challenge. This phenomenon occurs with many language models such as GPT-4, Gemini and others. Please question generated content critically and verify it, especially in a business context.
Conclusion
With this information, David could have been spared a lot of stress: Each of these models offers unique strengths and uses that can be leveraged depending on customer-specific business needs. It’s worth taking a closer look. Stay informed and regularly evaluate which model best suits your current and future plans.
Are you already familiar with objectiF RPM’s AI support? See for yourself! Feel free to contact us for a no-obligation trial – we look forward to seeing you grinning as relaxed as Lea in the near future.
[1] https://www.tuev-verband.de/pressemitteilungen/ein-jahr-chatgpt-gut-ein-drittel-nutzt-die-ki-fuer-unterhaltung-recherchen-und-inspiration-viele-davon-misstrauen-den-ergebnissen
[2] https://www.tuev-verband.de/pressemitteilungen/zwei-jahre-chatgpt
[3] https://backlinko.com/chatgpt-stats
[4] https://www.wsj.com/tech/ai/google-gemini-2025-chatgpt-openai-b6eb595d
[5] https://t3n.de/news/deepseek-v3-das-maechtigste-open-source-ki-modell-kommt-aus-china-1665606/
[6] https://aws.amazon.com/de/blogs/germany/aws-und-mistral-ai-vereinbaren-eine-verstaerkte-zusammenarbeit-zur-demokratisierung-der-generativen-ki/
[7] https://neuroflash.com/de/blog/llama2-vs-chatgpt/