Large Language Models Explained: From GPT to Claude — What Decision-Makers Need to Know
Last updated: March 2026 · Reading time: 8 minutes
GPT, Claude, Gemini, Llama — the names change faster than quarterly reports. For decision-makers, keeping track is challenging. Which model is right? What does it cost? And what does the model choice mean for data privacy and vendor lock-in?
This article explains how LLMs work, which models are relevant in 2026, and how to make the right choice for your business.
How Large Language Models Work
An LLM is a neural network with billions of parameters, trained on vast amounts of text. It learns statistical relationships between words, sentences, and concepts. When you ask an LLM a question, it calculates the most likely continuation — token by token.
The Transformer architecture, introduced by Google in 2017, is the technical foundation of all modern LLMs. It enables the model to recognize relationships across long text passages.
Important for decision-makers: An LLM does not "know" anything. It has no database of facts. It generates text that is statistically plausible. That is why quality assurance by humans is not an optional add-on but a necessity.
GPT, Claude, Gemini, Llama: Which Model for Which Purpose
GPT (OpenAI): The best-known LLM. Strong at general tasks, large ecosystem, broad API availability. Data is processed in the USA.
Claude (Anthropic): Focus on safety and long context windows. Especially strong at processing long documents and at tasks that require nuance. European hosting available.
Gemini (Google): Deeply integrated into the Google ecosystem. Strong at multimodal tasks (text + image). Interesting for businesses already using Google Workspace.
Llama (Meta) and open-source models: Freely available, self- hostable. Maximum data control but higher operational overhead. Relevant for businesses with strict data privacy requirements.
The right choice depends on three factors: data privacy requirements, task type, and budget. arocom advises vendor-independently and recommends the solution that fits your requirements.
LLMs in Day-to-Day Business: Three Concrete Applications
Intelligent search: Instead of keyword search, an LLM enables semantic search on your website. Users ask questions in natural language and receive relevant answers. This requires a combination of LLM and vector database — an architecture that arocom integrates into Drupal projects.
Content workflows: Editors receive AI suggestions for headlines, meta descriptions, and summaries — directly in the CMS. This speeds up work without giving up editorial control.
Data extraction: LLMs extract structured information from unstructured documents: contracts, support tickets, customer feedback. What used to require manual effort happens in seconds.
The Right LLM Strategy for Your Business
Three principles for a sustainable LLM strategy:
1. No vendor lock-in. Build your architecture so that you can switch the underlying model. The LLM landscape changes fast — those who tie themselves to a single provider lose flexibility.
2. Data privacy first. Check before every integration: Which data is transmitted to the model provider? Where is it processed? Is there a data processing agreement?
3. Start small, learn fast. Begin with a limited pilot project — such as an AI-powered search on a subsection of your website. Measure the results. Scale what works.
Since 2012, arocom has built digital platforms. LLM integration is not a gimmick but a concrete development focus with over 160 projects of experience as a foundation.
Integrating LLMs into your Drupal platform?
Whether semantic search, content assistant, or chatbot: arocom advises vendor-independently and implements. Contact us.
What is a Large Language Model?
A Large Language Model (LLM) is an AI system with billions of parameters, trained on vast amounts of text data. It understands and generates human language, forming the technical foundation for chatbots, content assistants, and semantic search.
What is the difference between GPT and Claude?
GPT (OpenAI) has the largest ecosystem and broadest API availability. Claude (Anthropic) excels with long context windows, European hosting, and a focus on safety. Both are suitable for enterprise applications — the choice depends on data privacy requirements and task type.
Can LLMs avoid hallucinations?
Not completely. Hallucinations are a systemic trait of statistical language models. The solution is not a better model but an architecture with Retrieval-Augmented Generation (RAG) and human quality assurance.
What does using an LLM cost for a business?
API costs range from a few cents to several euros per query depending on the model and usage volume. The real costs come from integration, training, and quality assurance. arocom advises on realistic total cost estimates.
How does arocom integrate LLMs into Drupal?
Via API interfaces to GPT, Claude, or open-source models, combined with vector databases for Retrieval-Augmented Generation. Integration happens through Drupal modules, so editors can use AI features directly in the CMS.
Read more
- AI for Businesses — The complete overview
- Generative AI in Enterprise Use — Opportunities and risks
- Prompt Engineering — Better results from LLMs
- Vector Databases — The infrastructure for RAG
- AI Integration as a Service — What arocom offers
Discover a random article
Questions about this topic? We'd love to help.
CMS Comparison 2025
Drupal vs. WordPress vs. TYPO3: An objective comparison for enterprise projects.
Was this article helpful?