Models
The language models available in meinGPT, their strengths, and recommendations for usage
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).
- Best model — heuristic auto-selection; defaults to EU-hosted models only.
Hinweis
Data protection: EU-hosted models process all data within the EU. For assistants, you must set the model explicitly — an EU model is not chosen automatically.
Recommendations for everyday use
| Use case | Recommendation |
|---|---|
| Standard chat (default) | GPT-5.1, GPT-5.5, Claude Sonnet 4.6 |
| Deep reasoning, strategy, audits | GPT-5 Thinking, o3, Gemini 2.5 Pro |
| Coding and refactoring | Claude Sonnet 4.6, Claude Opus 4.8, GPT-5.4 |
| Very long documents / 1M context | GPT-5.4, GPT-5.5, Claude Opus 4.8, Gemini 2.5 Pro |
| High volume, high throughput, low cost | GPT-5 mini, GPT-5 Nano (free), Gemini 2.5 Flash |
| Web research with source citations | Web Search (Linkup) |
| Default for assistants with tool calls | o3, GPT-5 Thinking |
Tipp
When in doubt, start with a regular (non-reasoning) model. Only switch to a reasoning model if the answers aren't good enough — reasoning consumes noticeably more credits.
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.
| Task type | Standard LLM | Reasoning model |
|---|---|---|
| Simple text, emails, summaries | Yes | No (too expensive) |
| Creative writing | Yes | No |
| Complex logic, mathematics, formal analysis | No | Yes |
| Multi-step planning | Sometimes | Yes |
| Coding (simple) | Yes | No |
| Coding (complex / architectural) | Sometimes | Yes |
| Compliance analysis, audits | No | Yes |
Tipp
Many models (the GPT-5 family, Claude, and Gemini 3.5 Flash) let you choose the reasoning effort — minimal, low, medium, or high — directly in the chat via the model selector. Higher effort means more thorough answers but takes longer and uses more credits. This lets you dial reasoning up or down per message without switching models. Dedicated reasoning models like GPT-5 Thinking and o3 remain available as separate, always-reasoning choices.
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.
| Model | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|
| GPT-4o Mini | EU | — | 128k | Classification, tagging, simple summaries, FAQ bots |
| GPT-4o | EU | — | 128k | General assistant, coding, multimodal, multilingual |
| GPT-4.1 | EU | — | 1M | Knowledge work, consistent output, tool use, long documents |
| o4-mini | EU | ✓ | 200k | Efficient reasoning tasks, many requests with a logic component |
| o3 | EU | ✓ | 200k | Structured reasoning, multi-step workflows, tool calls in assistants |
| o3-pro | US | ✓ | 200k | Highly demanding logic, formal analysis, compliance, audits |
| GPT-5 | EU | — | 400k | High-quality text, code, agents, long contexts |
| GPT-5 Thinking | EU | ✓ | 400k | Difficult logic, planning, mathematics, deep-dive analysis |
| GPT-5 mini | EU | — | 400k | Many requests, simple tasks, classification, pipelines |
| GPT-5 Nano | EU | — | 400k | Free for basic conversations and highly cost-sensitive bulk processing |
| GPT-5.1 | EU | — | 400k | Standard model for business and development, agents, planning |
| GPT-5.2 | EU | ✓ Coding | 400k | Coding-heavy workflows that need reasoning (EU) |
| GPT-5.2 (US) | US | ✓ Coding | 400k | Same class as GPT-5.2, US region |
| GPT-5.3 Chat | US | ✓ Coding | 128k | Chat-oriented variant with reasoning and coding focus |
| GPT-5.4 | EU | ✓ Coding | 1M | Very long contexts, heavy reasoning and coding tasks (EU) |
| GPT-5.4 Pro | US | ✓ Coding | 1M | Maximum reasoning + 1M context for the most demanding workloads |
| GPT-5.5 | EU | ✓ Coding | 1M | Previous EU flagship: reasoning, coding, long contexts |
| GPT-5.6 Sol | EU | ✓ Coding | 1M | New EU flagship: the most demanding reasoning, coding, and agentic work |
| GPT-5.6 Terra | EU | ✓ Coding | 1M | Balanced everyday model in the GPT-5.5 class at lower cost |
| GPT-5.6 Luna | EU | ✓ Coding | 1M | Fast and affordable for high throughput, still strong reasoning |
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).
| Model | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|
| Claude Haiku 4.5 | EU | — | 200k | Fast, cheap Anthropic responses for simple tasks |
| Claude Sonnet 4 | EU | — | 200k | Advanced coding, good balance of intelligence and speed |
| Claude Sonnet 4.5 | EU | — | 200k | Production coding, complex agents, multi-tool workflows |
| Claude Sonnet 4.6 | EU | — | 200k | Efficient Sonnet all-rounder with high speed |
| Claude Sonnet 5 | EU | — | 1M | Current Sonnet with 1M context, balanced speed and quality for coding, analysis, and agentic workflows |
| Claude Opus 4.1 | US | — | 200k | Agentic tasks, large refactorings, software engineering |
| Claude Opus 4.5 | EU | — | 200k | Opus class with EU hosting for high-quality requirements |
| Claude Opus 4.6 | EU | — | 200k | Current EU Opus for the most demanding tasks |
| Claude Opus 4.7 | EU | — | 1M | Opus with 1M context for very long documents and codebases |
| Claude Opus 4.8 | EU | — | 1M | Current Opus with 1M context, sharper agentic coding and stronger reasoning |
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.
| Model | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|
| Gemini 2.5 Flash | EU | ✓ | 1M | Bulk processing, fast APIs, image analysis |
| Gemini 2.5 Pro | EU | ✓ | 1M | Complex code projects, STEM, large datasets, agentic workflows |
| Web Search | EU | ✓ | 200K | Fast, cited web answers using Claude Haiku 4.5 and Linkup search |
| Gemini 3 Flash (Preview) | US | ✓ | 1M | Next-generation Flash — preview |
| Gemini 3.5 Flash | EU | ✓ | 1M | Latest Flash, combines reasoning at Pro level with high speed, multimodal input (text, image, audio, video) |
| Gemini 3.6 Flash | Global | ✓ | 1M | Newest Flash, reasoning at Pro level at nearly halved latency, multimodal input (text, image, audio, video) |
| Gemini 3.5 Flash-Lite | EU | ✓ | 1M | Budget-optimized for speed and high volume, with reasoning and multimodal input |
| Gemini 3.1 Pro (Preview) | US | ✓ | 1M | Multimodal reasoning, long contexts, agentic workflows — preview |
Mistral
Mistral models are hosted in European data centers and offer a strong EU-native alternative for business applications.
| Model | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|
| Mistral Medium 3 | EU | — | 128k | Enterprise deployments, multimodal analysis, long documents, coding |
| Mistral Medium 3.5 | EU | — | 256k | Latest Mistral Medium generation with larger context |
| Magistral Medium 1.2 | EU | ✓ | 128k | EU reasoning alternative for logic and analysis tasks |
| CodeStral | EU | — Coding | 256k | IDE code completion, fill-in-the-middle, code tests |
DeepSeek
DeepSeek models run via Azure AI Foundry and offer cost-efficient reasoning and coding options.
| Model | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|
| DeepSeek-R1 | Global | ✓ | 128k | Mathematical proofs, chain-of-thought, logic puzzles |
| DeepSeek-V3.2 | Global | — Coding | 128k | Cost-efficient batch processing, coding (Python/C++), multilingual |
Other models
| Model | Provider | Hosting | Reasoning | Context | Good for |
|---|---|---|---|---|---|
| Llama 3.3 | Meta (hosted on Nebius) | EU | — | 128k | Enterprise RAG, tool use, JSON output, on-premise scenarios |
| GPT-OSS 120B | OpenAI open weights (hosted on Nebius) | EU | ✓ | 128k | Self-hosting, RAG, on-prem agents |
| Kimi K2.5 | Moonshot (hosted on Azure AI Foundry) | Global | ✓ Coding | 131k | Reasoning and coding tasks with large context |
| Kimi K2.6 | Moonshot (hosted on Azure AI Foundry Sweden) | Global | ✓ Coding | 256k | Successor to K2.5 with doubled context window |
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.
Tipp
If meinGPT seems to give "worse" results than ChatGPT, it is almost never the model. State tone, role and format explicitly in your prompt, enable the tools you need - or store both permanently in an assistant.
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