The AI hype has been fuelled in particular by the development of so-called Large Language Models (LLMs). We present some of the best-known and most powerful LLMs and explain the differences between them:
An overview of the best-known LLMs
(Last updated: July 2026)
Was ist ein LLM und wie funktioniert es?
A large language model is a machine learning model trained to understand and generate human language. LLMs are based on what is known as the Transformer architecture and have billions of parameters. These are the learned weights that determine how the model responds to an input. An LLM is trained on vast amounts of text, primarily from the internet but also from other sources, enabling it to grasp linguistic structures such as grammar, meaning, context and factual knowledge.
Before processing, the text is broken down into tokens – the smallest units representing words, syllables or individual characters. The context length (e.g. ‘128k tokens’ in ChatGPT) specifies how many tokens a model can keep ‘in memory’ at any one time during a session: the larger the context window, the longer the documents or conversation threads the model can process coherently. Billing for API usage is usually calculated per token, with input and output tokens charged separately.
Nowadays, it is standard practice for models to be ‘multimodal’. This means that, in addition to plain text, they are capable of processing audio, video and other file formats. There is increasing talk of ‘foundation models’ or ‘multimodal models’; strictly speaking, the term ‘language’ no longer adequately captures their scope.
Key LLM developments in 2026
The key LLM trends for 2026 are the shift from pure text generators to multimodal, agent-based systems with a stronger focus on reasoning. Furthermore, control and security are coming to the fore in practical business applications. LLMs have evolved from experimental AI models into production-ready enterprise AI. Models think independently, control software and workflows, and coordinate autonomously within teams of agents. Here are the key topics and developments at a glance:
Welches sind die wichtigsten LLMs?
(Letzte Aktualisierung: Mai 2026)
The key LLMs in 2026 are the Frontier models from OpenAI, Anthropic and Google as the best-known providers. Strong open-source alternatives are Meta, Mistral AI and DeepSeek. Each offering different strengths depending on the application. The choice of provider determines the model’s efficiency, costs and data protection. A key difference for businesses also lies in control and deployment: proprietary models such as GPT, Claude or Gemini offer very high, stable performance, but are often only accessible via APIs and allow only limited customisation. As closed models, they are proprietary products of the companies in question, and their use is subject to a fee and restrictions. Open-source LLMs (open models), on the other hand, are publicly accessible and can be freely used and customised. These include, for example, LLaMA, Mistral and Falcon. They enable high data protection standards but require in-house hosting, which entails infrastructure costs and greater technical responsibility.
Proprietary LLM
GPT models from OpenAI
Which GPT model is the latest from OpenAI?
GPT-5.6 Sol is our most powerful model to date.
(OpenAI, 2026)
The latest model to be announced is GPT-5.6 (in preview since 26 June 2026), available in three sizes: Sol as the flagship model, Terra as a balanced, everyday option, and Luna as a fast, affordable option. It is currently a limited preview, available only to selected partner organisations via the API and Codex. The focus is on improved agent-based capabilities in programming, cyber security and complex, long-running tasks, including a new maximum reasoning mode and an ‘Ultra’ mode with sub-agents for task processing based on the division of labour. For most ChatGPT users, GPT-5.5 remains the generally available model at present.
Welche GPT-5-Versionen gibt es?
The ChatGPT app currently offers the Instant, Thinking and Pro models, which are based on GPT-5.5 (GPT-5.2 was replaced on 12 June 2026; ongoing conversations were automatically carried over). Other models:
- GPT-5.6 (Sol, Terra, Luna): Limited preview since the end of June 2026; so far only available via API and Codex to selected partners
- GPT-5.5 Pro: Top-tier variant for demanding tasks in science, finance and law GPT-5.5 Thinking: specialises in deeper logical analysis
- GPT-5.4 mini: a faster, cost-optimised all-rounder, used, amongst other things, as a fallback when rate limits are reached for Thinking modelsSpecialised models: for developers, including
- GPT-5.3-codex (software architecture, now integrated into GPT-5.4)
We have compiled more detailed information on OpenAI’s latest GPT model in our in-depth article on GPT-5.
What are the previous models of the GPT?
GPT-4o, GPT-4.1 and GPT-4.1 mini were removed from ChatGPT on 13 February 2026, but remain available via the API for the time being. GPT-4.5 followed on 26 June 2026. Earlier GPT-5 versions (GPT-5, GPT-5.1, GPT-5.2) have also disappeared from ChatGPT. The GPT-4 era was primarily dedicated to establishing reliable adherence to instructions and native multimodality. Dedicated reasoning models from the o-series were largely integrated into the ‘Thinking’ capabilities of the 5 series.
Do you have any more questions about ChatGPT? We’ve answered the most important questions about OpenAI’s ChatGPT here in a separate article.
Claude models from Anthropic
The model variants follow a hierarchical structure with different size and performance classes. The Claude 3 series has now been completely phased out (retired), whilst the Claude 4 series continues to form the operational backbone of the portfolio, supplemented since June 2026 by a new performance class above Opus, known as the “Mythos class”.
Please note: Access to Fable 5 and Mythos 5 was temporarily suspended between 12 June and 1 July 2026, following an order from the US Department of Commerce under export control regulations. Access to both models has been restored since 1 July 2026.
Anthropic, a company founded in 2021 by a number of former developers at OpenAI – the company behind ChatGPT – is a major competitor with its Claude series. Claude is characterised by its focus on safety, ethical governance and so-called ‘Constitutional AI’ principles; in other words, the model’s behaviour is guided by a set of governing principles to ensure it provides reliable responses. This makes it particularly well-suited for use in context-sensitive business applications. Furthermore, large context windows (up to 1 million tokens), multimodality and enhanced agent capabilities are core features, such as the autonomous execution of multi-step tasks and – more recently – scheduled, time-controlled agent workflows (“Managed Agents”). Claude is used, amongst other things, for productive tasks such as customer service chatbots and knowledge work, as well as for coding tasks and workflow automation.
All models are available via the Anthropic API as well as through integrations with partner cloud platforms (e.g. Amazon Bedrock, Google Cloud). Anthropic is now also generally available via Microsoft Foundry on Azure: there, Claude Opus 4.8 and Claude Haiku 4.5 are available alongside the GPT models as “Model-as-a-Service” (MaaS) via the Messages API.
Google und Gemini
In 2026, Google ranks among the leading providers of multimodal large language models with its Gemini series, positioning Gemini as a personalised, agent-based AI system that is deeply integrated into the Google ecosystem. Furthermore, Gemini supports integration with numerous Google services as well as selected third-party services. The current model family is based on the Gemini-3 generation.
The Gemini 3 family was officially unveiled in November 2025 and represents Google’s most powerful generation of models to date. Over the course of 2026, it was expanded to include further variants such as Gemini 3.1 Pro and Gemini 3 Flash. The model family focuses on advanced reasoning, agent-like capabilities and native multimodality. Google is thus addressing both complex scientific and technical tasks and productive applications in development, research and knowledge work. Key features include large context windows, multimodal processing and an efficient mixture-of-experts architecture. The Gemini series was developed by Google DeepMind as the successor to the LaMDA and PaLM model families. A detailed breakdown of the individual versions and development stages can be found in our article ‘Google Gemini explained: An overview of the Gemini AI models’.
The world’s best model for multimodal understanding and our most powerful agentic vibe-coding model to date.
- Gemini-API, 2025, on Gemini-3-Pro
Grok AI
Grok AI is the LLM family developed by xAI and was designed as a dialogue-oriented AI model with close integration with real-time information. What sets Grok apart is its integration with the X platform and its ability to incorporate up-to-date information into its responses. The current generation of models is based on Grok-4 models, which have been further developed compared to earlier versions such as Grok-3 and Grok-3 Mini, particularly in terms of reasoning and more complex tasks. In addition to higher-performance variants, xAI also offers faster and more cost-effective model versions for applications requiring higher throughput.
Strengths and weaknesses of Grok models
One of Grok’s key strengths is its connection to dynamic information sources and up-to-date data. This makes the model particularly well-suited to current topics, research tasks, interactive conversations and the rapid processing of information. Furthermore, Grok is used for creative writing, programming support and general AI assistant applications.
Like other generative AI systems, Grok also faces challenges in the areas of security and governance. In particular, the handling of sensitive content, the quality of real-time information and the prevention of problematic outputs are recurring points of criticism regarding Grok and remain key areas for development with a view to the professional use of the models.
Grok is available via various interfaces, such as web applications, the X platform and mobile applications. In addition, xAI makes the models available to developers and businesses via APIs.
Open-Source LLM
Llama Models
In February 2023, Facebook’s parent company, Meta Platforms, also entered the field of large language model development with the release of LLaMA (Large Language Model Meta AI). Several generations of the model have been introduced since its initial release. Well-known open-source models include the following:
The current generation of models is the Llama-4 family (released in 2025), which is based on a mixture-of-experts architecture. The models are designed to be multimodal and support image processing as well as text processing. Furthermore, the models have been optimised for multilingual applications and for use in various AI applications. Meta makes model weights, model cards and developer documentation publicly available, enabling Llama to play a key role in the open-source AI ecosystem, particularly for research, customisation and enterprise applications.
We have optimised our models for ease of deployment, cost-effectiveness and scalable performance for billions of users. We are excited to see what you will build with them. – MetaAI, 2025
The Llama-4 models are used in various meta-products and are integrated, amongst other places, into the AI assistants on WhatsApp, Messenger and Instagram, as well as via the web. Furthermore, developers can use the models via various cloud and AI platforms and customise them for their own applications.
Mistral/Mixtral by Mistral AI
The French AI company Mistral AI is one of the best-known providers of efficient open-weight models. The company was founded in 2023 by former employees of Google and Meta Platforms and received early investment from Microsoft, amongst others. With its powerful and efficient models, Mistral AI is positioning itself as a European alternative to established US AI providers.
Some Mistral AI models are available as open-weight models and can be adapted and reused by developers for their own applications. In doing so, the company is pursuing an approach that combines openness, efficiency and flexible deployment options in the development of generative AI applications.
Of particular relevance to businesses is Mistral AI’s European base, as well as the ability to utilise models via various deployment options. This allows for better compliance with data protection and data processing requirements.
The current flagship model is Mistral Large 3, a powerful large language model with a mixture-of-experts architecture. It was developed for demanding tasks such as complex reasoning, programming and knowledge processing, and adds a powerful open-weight model to the model family for productive AI applications.
In addition to the models mentioned above, Mistral AI offers Le Chat, an AI chatbot which, much like ChatGPT, can be used for entertainment, text generation and interactive applications:
What developments are taking place outside Europe and the US?
The global LLM market is highly decentralised and very dynamic, with new providers regularly entering the market with high-performance models. Regional providers are gaining in importance due to their cultural specificity and extreme cost-efficiency. Numerous companies and research institutes are developing their own models tailored to specific markets, languages or use cases. Examples include Cohere with its Command models, as well as initiatives such as AI4Bharat and SEA-LION, which focus on linguistic diversity and regional applications.
Regional specialisation and autonomy
Specialised ‘Sovereign AI’ projects enable independence from Western datasets. Some providers that are holding their own include the following:
- South-East Asia (SEA-LION): The SEA-LION model, developed by AI Singapore, has been specifically trained for South-East Asian languages and cultural contexts. The focus is on improving the processing of regional languages and increasing the representation of local content.
- India (Sarvam AI & AI4Bharat): India is investing heavily in its own AI infrastructure and models to better reflect the country’s linguistic diversity. Companies and research initiatives are developing models that specifically support Indian languages and local application scenarios.
- South Korea (Samsung Gauss & Upstage): South Korean companies are developing their own AI models with a focus on enterprise applications, product integration and local language processing. Samsung Electronics uses its AI models primarily for integration into its own device and software ecosystems, whilst Upstage focuses on enterprise solutions and specialised language models.
China as a technological counterweight
China has emerged as a key player in the global LLM race and is focusing in particular on high-performance open-weight models and its own AI ecosystems. Several major technology companies and start-ups are building their own large language models to compete with the US, including:
- DeepSeek: The Chinese AI start-up from Hangzhou offers the DeepSeek-R1, V3 and other models as open-weight, cost-effective, high-performance LLMs in emerging markets.
- Baidu (ERNIE) and Alibaba Cloud (Qwen series): Two established tech conglomerates with their own LLM stacks and chatbot applications, often with a strong focus on the Chinese language and local market specifics.
- Zhipu AI (GLM family): One of the major competitors in China’s LLM ecosystem, with international branches and several model variants.
Key Global Trends
Three key trends in 2026 extend beyond the regional focus:
- Agentic Autonomy: Models act as ‘agents’ to autonomously perform complex task chains (travel bookings, coding, analysis).
- Ultra-Long Context: A context window of 1 million tokens is the global standard; leading models (such as Llama 4 Scout) can handle up to 10 million.
- On-Device and Edge: The shift from the cloud to local hardware (smartphones, IoT) has reached market maturity thanks to highly efficient small models.
These developments form the basis for the use of large language models within organisations to make customer communications more efficient and personalised.
Development and Use of LLM in Customer Communications
Proprietary models from major providers, including GPT-5, Gemini 3 and Claude 4, continue to dominate in terms of performance, multimodality, and product integration. The further development of these models is focusing on complex reasoning, agent and enterprise capabilities. At the same time, open-source models, such as those from Meta, Mistral or Asian providers, are coming to the fore and offering realistic alternatives for businesses. Governance, data protection and regulatory requirements play a central role in this, particularly in Europe. Companies must embed LLMs not merely as a technology, but as a strategic building block within their processes and data landscapes.
Despite the impressive capabilities of large language models (LLMs), experience shows that they are not always the best choice for direct customer communication. Often, the responses provided are too generic; integration into existing systems (such as CRM or ticketing systems) can be problematic, or these systems require extensive customisation to take brand identity and industry-specific knowledge into account. Specialised solutions, such as moinAI, generally ensure precise and GDPR-compliant customer interactions more quickly.
Discover how moinAI can help your business with the smart automation of customer enquiries and why it is the ideal partner for professional customer communication.
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