Four language models, the same knowledge base

This page compares four chatbots directly. All four answer questions solely on the basis of the content of this website. They use the same software and the same process: the question is analysed, relevant content is searched for in the index, and then processed by the language model into an answer. This method is called Retrieval-Augmented Generation – RAG for short.

What differs at the core is the language model and – provided the respective provider offers its own embedding model – the embedding model as well. Mistral, Claude, GPT and Gemini – four providers, one and the same knowledge.

Why this can be compared at all

RAG separates knowledge and model. The knowledge resides in a dedicated vector database and comes from your own documents. The language model processes the retrieved information and formulates the answer from it. This makes the language model an interchangeable component: it can be swapped without having to rebuild the knowledge base. That is exactly what you see here, four times over.

The language model is interchangeable

The software behind all four chatbots is dAi Pro; how requests are processed and logged is described under GDPR-compliant AI and in the information window ("?") of each chatbot.

dAi Pro is not tied to a specific AI provider. Depending on the requirement, different language and embedding models can be used – for example with a focus on answer quality, speed, cost or European data processing.

The knowledge base and application remain unaffected by this. Companies can switch the model in use without having to rebuild their content or their RAG application.

What you can look out for when comparing

Ask all four chatbots the same question one after another. Compare less the underlying facts – these come from the same sources – and more the way each model generates an answer from them:

• Length and structure. Does the model answer concisely and directly, or in detail and structured form?
• Handling knowledge gaps. What happens when the knowledge base does not contain a sufficient answer? Is this communicated transparently, or does the model supplement information from its own knowledge?
• Source citations. What evidence is cited, and does it support the statement?
• Speed. The response times differ considerably.

How to read this comparison

This comparison is not a scientific benchmark, but a practical snapshot based on a manageable knowledge base.

There is an important technical difference between the four configurations: each of the four configurations has its own vector index. If a provider supplies its own embedding model, that model is also used for the respective index. As a result, even the retrieved text passages can differ slightly – before the language model even formulates the actual answer. Models, prices and response behaviour also change constantly. A comparison only becomes meaningful with your own documents and your own questions.

Costs

Alongside answer quality and data protection, a third factor determines the ongoing operation of a RAG application: the price per token. Between the four providers, the difference is not measured in percentages but in orders of magnitude.