The most important AI models: an overview
The number of language models is growing, each with its strengths and weaknesses. But which model is right for your specific use case, your company? Here’s an overview.
It’s February 2025, almost exactly two years since ChatGPT first caused a stir and brought AI into the public spotlight. Today, a year later, new AI models are sprouting up like mushrooms – the latest surprise, DeepSeek, has shaken the AI world once again. However, it’s quickly clear that following every hype is not the way forward: each model has its strengths and weaknesses, and companies need to make a strategic decision when choosing an AI model.
This article sheds light on the topic of AI models: Which are the most important models on the market right now? What use cases are they suited for? And how should companies decide on the right model?
Disclaimer: The following information is based on the subjective experiences that the ONTEC AI team has gathered until February 2025. The article does not claim to be exhaustive but aims to provide a rough overview. For the most current developments, we recommend additional research.
AI Model, Language Model, Transformer, LLM – What’s What?
In everyday life, we often come across the terms AI model, language model, transformer, or LLM, and these terms are often used interchangeably. Let’s briefly clarify the difference between these terms:
AI Model
The term “AI model” encompasses various approaches in artificial intelligence. Here are some well-known examples:
Regression Models:
Predict continuous values (e.g., stock prices, temperature).
Neural Networks & Deep Learning:
Particularly good for image recognition, language processing, and complex patterns.
Example: Convolutional Neural Networks (CNN) for image recognition.
Reinforcement Learning:
Learns through reward systems, often used for games or robotics.
Example: AlphaGo (chess & Go AI).
Classification Models:
Assign data to categories (e.g., spam filters, face recognition).
Generative AI:
Creates new content (e.g., text, images, music).
Example: ChatGPT (text), DALL·E (images).
Generating Responses and Drafts:
AI can generate reply drafts using flexible templates. These can either be sent automatically or handed over to employees for review and personalization.
For more information on the most important AI vocabulary, check out our AI glossary!
How does an AI model Work?
- Training: The model is trained with large amounts of data.
- Optimization: An algorithm adjusts weights and parameters.
- Evaluation: The model is tested and optimized.
- Deployment: It is used in real-world applications (e.g., chatbots, image recognition).
How can AI models be applied?
AI models can be applied in manifold ways, for example:
- Language Processing: ChatGPT, Google Assistant, Siri
- Image Recognition: Face recognition, medical diagnostics
- Automation: Robots in industry
- Recommendation Systems: Netflix, Amazon, YouTube
- Autonomous Driving: Tesla, Waymo
Language Model / LLM
The well-known ChatGPT is also an AI model. More specifically, it is a large language model, or “LLM” (Large Language Model).
By the way: Language models differ in terms of their “size,” i.e., the number of parameters and the amount of data they are trained on. Smaller, specialized models can be superior in their field, especially when they are specifically trained.
Transformer
A Transformer is a neural network model introduced by Google in 2017, based on the self-attention mechanism, which allows it to efficiently capture context and understand long text passages. It is the foundation of almost all modern LLMs because it enables scalability, parallel processing, and high-quality language processing, which makes models like GPT, LLaMA, Mistral, and others powerful.
ChatGPT is often referred to as GPT (Generative Pre-trained Transformer). Many people mistakenly think that GPT models actively learn during their operation. In fact, they are pre-trained, meaning they learn during an extensive training phase using large data sets. Improvements occur only through retraining and the release of a new version (e.g., the transition from GPT-3 to GPT-4).
This article will now focus on language models (LLMs), a subset of AI models.
Overview of the Most Relevant Language Models: Strengths, Weaknesses, and Recommended Use Cases
As of February 2025, the following providers and language models are particularly widespread:
OpenAI (ChatGPT)
OpenAI, the developer of ChatGPT, is considered the leader among LLM providers. Its models, including the latest GPT-3o, are known for their versatility and ability to understand and generate natural language.
- Strengths: Very versatile, excellent at understanding and generating natural language, handles a variety of tasks from text completion to translations; multimodal (text, audio, image, including images via DALL-E); data processing for diagrams; extensive third-party integrations; can also be hosted in Azure Cloud and be GDPR-compliant; frequently mentioned as the leader in security and suppression of inappropriate content.
- Weaknesses: Requires significant computational resources, can be costly to deploy (especially if it needs to be GDPR-compliant); not open-source, so no direct fine-tuning. Certain content is censored, e.g., political figures.
- Recommended Use Cases: Customer service chatbots, content creation, translation, summarization, and complex text analysis.
LLaMA
LLaMA was developed by Meta AI (the company behind Facebook), and the current version is LLaMA 3.3. Its special feature is its open-source nature.
- Strengths: Efficient design, good performance on a variety of language tasks; open-source, allowing developers to customize and extend the model; can be easily deployed on various hardware, on-premises for users/companies.
- Weaknesses: Struggles with very complex tasks compared to larger models; requires expertise for fine-tuning and effective deployment; not multimodal.
- Recommended Use Cases: Research and development, educational tools, language-based applications requiring customization, and smaller NLP tasks, particularly for privacy-sensitive content.
Aleph Alpha
Aleph Alpha is a European model developer focusing on multilingual applications and European contexts. Its models, Pharia and Luminous, emphasize strong privacy and data security, making them particularly suitable for the European market.
- Strengths: Strong focus on European languages and contexts, robust performance in multilingual environments; designed for enterprise use with a focus on privacy.
- Weaknesses: Fewer third-party integrations compared to other models.
- Recommended Use Cases: Multilingual applications, market-specific solutions for Europe, enterprise applications with stringent privacy requirements, such as in privacy-sensitive environments.
ONTEC AI is an Augmented Intelligence platform that allows employees to query and utilize the company’s internal data at any time, using the chosen LLM.
Claude
Claude – specifically, the models Hiaku, Sonnet, Opus – was developed by Anthropic. Claude’s models emphasize safety and ethical considerations.
- Strengths: Focus on safety and ethical considerations, designed for better interpretability and controllability; very good support for coding, particularly in Python.
- Weaknesses: Less conversationally strong than other models; responses are less detailed but more concise.
- Recommended Use Cases: Ideal for conversational AI applications like customer support, virtual assistants, ethical AI applications, and scenarios requiring high interpretability and control; coding.
Gemini
Gemini, developed by Google DeepMind, is known for its performance on multimodal tasks.
- Strengths: Excellent for complex data analysis and applications requiring a combination of text and image processing; robust architecture.
- Weaknesses: High computational requirements, complexity in deployment and fine-tuning; the biggest weakness is data privacy, as some data is sent back to the USA.
- Recommended Use Cases: Multimodal applications, such as combining text and image analysis; coding.
DeepSeek
DeepSeek, developed by a Chinese startup, has shocked the world due to its cost-efficiency and powerful design.
- Strengths: Specializes in search and information retrieval, requires significantly less computational power than competitors and can be operated at a lower cost; available as an open-source model; “Reasoning” feature shows how the AI arrives at its results.
- Weaknesses: Limited to search-related tasks, may require significant customization for specific use cases; privacy concerns (which could be mitigated by self-hosting); certain content is censored.
- Recommended Use Cases: Corporate search engines, information retrieval systems, knowledge management, and document indexing.
Mistral
Mistral offers a high-performance AI model with its current model 7B, designed for efficiency and scalability. Developed by an innovative team specializing in natural language processing, Mistral is especially suited for large-scale NLP tasks and data-intensive applications requiring high performance.
- Strengths: Designed for high efficiency and performance, excellent at natural language processing tasks, scalable for large datasets; open-source and customizable; no API dependency; GDPR-compliant.
- Weaknesses: May require significant computational resources, less known and possibly fewer community resources compared to more established models.
- Recommended Use Cases: Large-scale NLP tasks, data-intensive applications, and scenarios requiring high performance and scalability.
Comparison of All Models
| Model | Open Source | Features |
|---|---|---|
| GPT (OpenAI) | No | Proprietary, commercial, very powerful |
| Gemini (Google DeepMind) | No | Multimodal (Text, Image, Code), advanced |
| Claude (Anthropic) | No | Focus on safety, “Constitutional AI” |
| LLaMA (Meta) | Yes | Efficient, for researchers and developers |
| Mistral | Yes | Lightweight, powerful, European |
| DeepSeek | Yes | Open-source alternative from China |
| Aleph Alpha | No | European, privacy-friendly |
For more details and a comprehensive comparison of AI models, we recommend checking out this in-depth overview.
How Do I Choose the Right Language Model?
To choose the right LLM, some considerations must be made.
- Consider the individual challenge: What task should the model help with? Text creation, data analysis, research, programming, etc.?
- Base it on the IT system and data: What is the current setup? Which IT system should the model be integrated into, and what kind of data will it work with?
- Hosting: Where should the model be hosted, in the cloud or on-premises?
- Compare models: Data availability, interpretability, computational resources, scalability… Models differ in many details, which are not always immediately obvious. A technical comparison is crucial and leads us to the next point.
- Consult experts: Use the expertise of internal and external professionals. Consulting experts can provide valuable insights and help with decision-making.
- Experiment with different models: A “test drive” with the respective model will help determine if the results meet expectations.
- Stay flexible: Many applications allow switching between different models. Look for adaptable software solutions where you are not tied to a specific model.
Summary and Key Takeaways
Choosing the right language model requires understanding the strengths and weaknesses of various models and considering factors such as the nature of the problem, the existing IT system, and more.
Through careful consideration by technically skilled employees or external experts, companies can determine the right LLM for their needs.
Also, practical testing is helpful to determine which LLM delivers the best results for their needs.
It is clear that the development of existing models and the creation of entirely new models is advancing, and companies should remain as flexible as possible.