Available Models

Chat AI provides a large assortment of state-of-the-art open-weight Large Language Models (LLMs) which are hosted on our platform with the highest standards of data protection. The data sent to these models, including the prompts and message contents, are never stored at any location on our systems. Additionally, Chat AI offers models hosted externally such as Anthropic Claude and OpenAI GPT-5.

Available models are regularly upgraded as newer, more capable ones are released. We select models to include in our services based on user demand, cost, and performance across various benchmarks, such as HumanEval, MATH, HellaSwag, MMLU, etc. Certain models are more capable at specific tasks and with specific settings, which are described below to the best of our knowledge.

List of open-weight models, hosted by GWDG

List of external models, hosted by external providers

Open-weight models, hosted by GWDG

The models listed in this section are hosted on our platform with the highest standards of data protection. The data sent to these models, including the prompts and message contents, are never stored at any location on our systems.

DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is a strong Mixture-of-Experts (MoE) model with 304B parameters and a context window size of one million tokens. It outperforms DeepSeek V4 Pro (Preview) on several benchmarks despite its smaller activated parameter count, and is suitable for complex agentic workflows.

Google Gemma 4 31B Instruct

Gemma 4 models offer frontier-level performance, well-suited for reasoning, agentic workflows, coding, and multimodal understanding.

GLM 5.3 Flash

GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series. It is a hybrid-architecture MoE model with 320B total parameters and just 18B active, combining sparse and linear attention with Manifold-Constrained Hyper-Connections (mHC) to deliver precise long-context capabilities at significantly reduced serving cost. It outperforms GLM-5.2 across benchmarks and real-world workloads while approaching Claude Opus 4.8 on coding and agentic benchmarks.

Meta Llama 3.1 8B Instruct

The standard model we recommend. It is the most lightweight with the fastest performance and good results across all benchmarks. It is sufficient for general conversations and assistance.

Qwen 3 30B A3B Instruct 2507

This MoE model features 30.5B total parameters with 3.3B activated parameters for efficient inference. It delivers significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage, with better alignment for subjective and open-ended tasks. The model supports a 256K native context length and operates in non-thinking mode, achieving strong performance across knowledge, reasoning, coding, and multilingual benchmarks.

Qwen 3 Coder Next

Qwen 3 Coder Next has 80 billion total parameters and 3 billion activated parameters which makes it cost-effective, especially for agent deployment. With a context length of 256K and adaptability to various templates, it can also be integrated in CLI and IDE platforms.

Qwen 3 Omni 30B A3B Instruct

Qwen3 Omni is a natively multilingual omni-modal foundation model that processes text, images, audio, and video. It achieves state-of-the-art performance on many audio/video benchmarks with ASR, audio understanding, and voice conversation performance comparable to Gemini 2.5 Pro. The model features a novel MoE-based Thinker–Talker architecture with AuT pretraining, supports 119 text languages, 19 speech input languages, and enables low-latency interaction with flexible control via system prompts.

Qwen 3.5 397B A17B

Qwen 3.5 397B A17B is a MoE model with 397 billion total parameters and 17 billion activated parameters. It represents one of the most powerful open-weight models available, delivering exceptional performance across reasoning, coding, mathematics, and general tasks. The model supports vision capabilities, and provides state-of-the-art performance among open models.

Qwen 3.6 35B A3B

Qwen 3.6 35B A3B is an MoE model with 35 billion total parameters and 3 billion activated parameters for efficient inference. It is suitable for real-world tasks, including coding and agentic workflows.

Qwen 3.8 27B

Qwen 3.8 delivers substantial gains compared to its predecessors across research, coding, and large-scale agentic tasks. Qwen 3.8 is a native vision-language model that understands images and videos, with flexible thinking control, and is designed to complete complex tasks with greater reliability.

External models, hosted by external providers

Anthropic Claude Sonnet 5

Claude Sonnet 5 is built as an agentic model. It provides substantially improved cost efficiency at medium effort; its higher-effort performance can match Opus 4.8 on some tasks.

Anthropic Claude Sonnet 4.6

Claude Sonnet 4.6 provides an excellent balance between capability and speed. It is optimized for production workloads and delivers fast, reliable responses for coding, analysis, and conversational tasks.

OpenAI GPT-5, 5.1, 5.2, 5.3, 5.4, 5.5, and 5.6 Series

OpenAI’s GPT-5 series models achieve state-of-the-art performance across various benchmarks. The series consists of the following models along with their intended use cases:

• OpenAI GPT-5.6 Sol: The frontier model of the GPT-5.6 family, built for complex professional work.
• OpenAI GPT-5.6 Terra: The GPT-5.6 model that balances intelligence and cost.
• OpenAI GPT-5.6 Luna: The GPT-5.6 model optimized for cost-sensitive workloads.
• OpenAI GPT-5.5: Built for the most complex professional work, including coding use cases, tool-heavy agents, grounded assistants, and customer-facing workflows where execution quality and response polish are important.
• OpenAI GPT-5.4: Built for professional knowledge work, including document and spreadsheet tasks, coding, data analysis, agentic workflows, and software automation.
• OpenAI GPT-5.4 Mini: A lightweight variant of GPT-5.4 for cost-sensitive applications.
• OpenAI GPT-5.4 Nano: A highly optimized variant of GPT-5.4. Ideal for applications requiring low latency.
• OpenAI GPT-5/5.1/5.2: Designed for logic-heavy and multi-step tasks.
• OpenAI GPT-5 Mini: A lightweight variant of GPT-5 for cost-sensitive applications.
• OpenAI GPT-5 Nano: A highly optimized variant of GPT-5.

OpenAI GPT-4.1

OpenAI’s GPT-4.1-class models improve on the older GPT-4 series. These models also outperform GPT-4o and GPT-4o Mini, especially in coding and instruction following. They have a large context window size of 1M tokens, with improved long-context comprehension, and an updated knowledge cutoff of June 2024.

OpenAI GPT-4.1 Mini

This was developed as a more cost-effective and faster alternative to GPT-4.1.

OpenAI o1 and o1 Mini

OpenAI’s o1-class models were developed to perform complex reasoning tasks. These models have now been superceded by the o3-series, and are therefore no longer recommended.

OpenAI o3

Released in April 2025, OpenAI’s o3-class models were developed to perform complex reasoning tasks across the domains of coding, math, science, visual perception, and more. These models have an iterative thought process, and therefore take their time to process internally before responding to the user. The thought process for o3 models are not shown to the user.

OpenAI o3 Mini

This was developed as a more cost-effective and faster alternative to o3.