Models

meinGPT brings together leading language models (LLMs) from multiple providers under a single interface. For each chat, you choose the model that fits the task — based on the work to be done, the speed you need, and your data protection requirements.

Indicators in the model picker

The model picker shows several indicators to help you decide:

• EU flag — the model is hosted in the EU and is GDPR-compliant without further measures.
• US flag / globe — the model runs outside the EU.
• Quality and speed indicators — visible on hover, each rated 1–5.
• Reasoning tag — marks models that use an explicit thinking step before answering.
• Preview tag — the model is still in a preview phase (functionality and behavior may change).
• Free indicator — the model is not billed and is always available (currently GPT-5 Nano).
• Cost tier ($ to $$$$) — hovering shows roughly how expensive a model is compared to others ($ = cheap to $$$$ = expensive).
• Best model — selects GPT-5 first. Only if the conversation, including any attached documents, exceeds GPT-5's context window does it automatically switch to Gemini 2.5 Flash instead. The choice is based solely on how much input there is, not on task type or the reasoning depth required — for demanding tasks, you should still select a specific model manually. By default, only EU-hosted models are used this way.

Recommendations for everyday use

Reasoning models vs. standard LLMs

Reasoning models perform a visible thinking step before producing the actual answer. This delivers significantly better results on multi-step or logic-heavy tasks — at the cost of speed and credits.

Context window and file uploads

The Context column in the model tables below shows the context window in tokens - the total amount of text a model can consider at once in a request (chat history plus all attached files combined). As a rough rule of thumb, one A4 text page is about 500-750 tokens; a model with a 200k context window handles roughly 250-400 pages of plain text, one with 1M context correspondingly more.

If you hit one of these limits, switching to a different model (possibly from a different provider) usually helps - see Chat error messages for details on the individual error messages.

Models differ considerably in consumption: more capable models (currently including o3-pro, GPT-5.4, GPT-5.4 Pro, and the Claude Opus models) cost several times more per request than cheaper ones. The difference sits entirely in the consumption rate — there are no special classes. The exception is models explicitly marked as free, whose use consumes no credits.

OpenAI

OpenAI models mostly run via Azure OpenAI Service — for EU hosting, exclusively in EU data centers (Sweden Central). Individual US variants go directly via OpenAI. Data is not used for training.

Anthropic (Claude)

Claude models run via Google Vertex. Anthropic pioneered the Artifacts concept — accordingly, Claude models produce especially clean artifact output (interactive documents, diagrams, code snippets).

Google (Gemini)

Gemini models run via Google Vertex and stand out with the largest context windows (1M+ tokens). Ideal for long documents, entire knowledge bases, or extensive file uploads.

Mistral

Mistral models are hosted in European data centers and offer a strong EU-native alternative for business applications.

DeepSeek

DeepSeek models run via Azure AI Foundry and offer cost-efficient reasoning and coding options.

Other models

Image generation

Image models (FLUX family, GPT Image, Gemini image models) are documented separately under Image model management — including hosting region, premium status, and recommendations.

Enabling or disabling models

Which models your users can choose from in the workspace is controlled by you as an admin in the settings. Details: Workspace configuration.

Why do answers differ from ChatGPT, Claude or Gemini directly?

Even when you pick the same model in meinGPT (e.g. GPT-5.5), the answers can differ from what you get in ChatGPT, the Claude app or Gemini directly. This is not a matter of lower model quality - it is the same model from the same provider. The differences come from the environment the model runs in:

• A different system prompt. ChatGPT and others silently give the model their own heavily prepared system prompt and a fixed answer format. meinGPT uses a more neutral system prompt so the model follows your workspace and assistant settings. For consistent results, set tone and format directly in your prompt or in an assistant.
• No ChatGPT personalization. In ChatGPT, saved "custom instructions" and memory from earlier conversations often shape the answer - without you noticing. In meinGPT you start without that invisible context. You can bring your saved context across using the memory feature.
• Randomness is built in. Language models are not deterministic - the same question yields slightly different wording from one run to the next, even in the same tool.
• Tools and web search. Whether the model may search the web or use tools strongly affects the answer. Use the "+" menu to check that the right tools (e.g. web search) are enabled.

Why can meinGPT feel slower than ChatGPT or Claude natively?

It's the same model with the same weights - answer quality is identical. Small speed differences from the native app usually come down to:

• EU hosting: EU models deliberately run in European data centers (e.g. Azure Sweden Central, Google Vertex EU) rather than the fastest US regions. This routing path through our aggregation layer typically costs only fractions of a second, but can become noticeable.
• Provider load: response times at the AI provider fluctuate during peak times - this affects native apps just the same.

Sustained or sudden slowdowns are almost never a meinGPT server issue - they're provider-side. For a specific error message in chat, see Chat error messages.

Related pages

• Image models — image generation models and their use cases
• Workspace configuration — control which models are enabled per workspace
• Privacy — GDPR compliance, EU hosting, provider overview
• Pricing — token prices per model