The immense progress made over the last two years in the fields of artificial intelligence and natural language processing is opening up new areas of application for AI in science, business and everyday life. The AI hype has been driven 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 Most Popular LLMs
(Last updated: May 2026)
What Is an LLM and How Does It Work?
A large language model (LLM) is a machine learning model trained to understand and generate human language. LLMs are based on the so-called 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) indicates 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 per token, separated into input and output.
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 include 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:
What Are the Most Important LLMs?
(Last updated: May 2026)
Frontier models for LLMs in 2026 span models from OpenAI, Anthropic and Google – the best-known providers – as well as strong open-source alternatives from 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, and their use is subject to fees 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 or Falcon. They enable high data protection standards but require self-hosting, i.e. infrastructure costs and greater technical responsibility.
Proprietary LLM
OpenAI GPT Models
Which GPT model is the latest from OpenAI?
"GPT-5.5 is our most powerful model to date for agent-based programming." (OpenAI, 2026)
The latest flagship model is GPT-5.5 Pro (May 2026). It is regarded as one of the most powerful models for professional applications, particularly due to its strong reasoning capabilities and performance in agent-based tasks. More recent versions have expanded these capabilities, particularly in the field of autonomous systems. The focus is on reduced memory consumption and more cost-effective usage.
What versions of GPT-5 are available?
The ChatGPT application currently offers the Instant, Thinking and Pro models. All are based on knowledge up to at least August 2025. Other models include:
- GPT-5.5 Pro: The most powerful model for autonomous agent tasks
- GPT-5.4 Thinking: Specialised in deep logical analysis and research
- GPT-5.4 mini: An extremely fast, cost-optimised all-rounder
- Specialised models: For developers, including gpt-5-codex (software architecture) and the ultra-lightweight gpt-5-nano for edge applications and high throughput.
We have compiled more detailed information about OpenAI’s latest GPT model in our in-depth article on GPT-5 here.
What are the previous models of the GPT?
Earlier models such as GPT-4o and GPT-4.1 are now only offered as legacy options, primarily to support stable legacy systems within the API. The GPT-4 era was primarily dedicated to establishing reliable instruction adherence and native multimodality, the foundation for autonomous agent systems. Dedicated inference models from the o-series, such as o3/o3-pro, have largely been integrated into the new ‘Thinking’ capabilities of the 5-series. However, they remain available for highly specialised scientific computations.
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 been discontinued, whilst the Claude 4 series and the new hybrid reasoning models form the core of the portfolio:
Anthropic, a company founded in 2021 by a group of former developers from OpenAI – the company behind ChatGPT – represents a significant competitor with its Claude series. Claude is characterised by its focus on safety, ethical governance and so-called ‘Constitutional AI’ principles, meaning that the model’s behaviour is guided by a set of governing principles to provide reliable answers. It is therefore particularly relevant for use in context-sensitive enterprise applications. Furthermore, large context windows, multimodality and enhanced agent capabilities are core features, such as the autonomous execution of multistep tasks. Claude is used 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 integrations with partner clouds (e.g. Google Cloud Vertex AI, AWS Bedrock). Anthropic is now in partnership with Microsoft: the Claude-4 series is available there alongside the GPT models as ‘Model-as-a-Service’ (MaaS).
Google Gemini
By 2026, Google’s Gemini series will be among the leading providers of multimodal large language models and, as a personalised, agent-based system, will be deeply embedded within the Google ecosystem. Gemini also offers integration with many third-party services. The current models are those of the Gemini 3 series:
The latest version of Google’s LLM is Gemini-3, which was officially released in November 2025. The Gemini-3 family focuses on deep reasoning and agentic capabilities, and features more advanced reasoning and multimodal capabilities that supersede previous models. Google addresses both complex scientific and technical tasks as well as interactive applications in education and research, as the models are characterised by large context windows, multimodality, and an efficient mixture-of-experts architecture. Google DeepMind developed the Gemini series as the successor to the LaMDA and PaLM models. 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 is primarily a dialogue- and interaction-oriented LLM with real-time data access. The current version, Grok 4.1, has been specifically trained to deliver more empathetic, emotionally intelligible responses and to improve the quality of its conversations. Grok 4.1 Fast offers the same core model but focuses on response speed. Grok 3 and Grok 3 Mini cover older and lightweight use cases respectively.
Strengths and weaknesses of Grok models
What sets Grok apart is its close integration with dynamic information sources and platforms. As a result, the model performs particularly well with current topics, research-style queries and interactive conversations. The model represents a relevant alternative to established models for use cases such as real-time assistance, creative writing or rapid information processing. Grok 4.1 is available via the web, X and mobile apps. Furthermore, the models can be selected in different modes (‘Auto’ or explicitly ‘Grok 4.1’). However, Grok continues to be surrounded by various controversies: the most recent criticism in early 2026 arose due to problematic outputs and inadequate content moderation, particularly regarding sensitive or even, in some cases, pornographic content. This reinforces the fact that issues such as content control and governance are key areas of development for its continued use in professional environments.
Open-Source LLM
Llama Model Family
In February 2023, Facebook’s parent company, Meta, also entered the world of large language models and unveiled its LLM, MetaAI, known as LLaMA. Since its initial release, several model families have been introduced. Well-known open-source models include the following:
The current model version is the Llama-4 family (April 2025), which is based on a mixture-of-experts architecture. The models are multimodal, meaning they natively support both text and image inputs and are designed for multimodal tasks. They are also multilingual. Meta is developing the models openly and making weights, model cards and developer documentation publicly available for research, product integration and generative AI applications.
We have optimised our models for ease of deployment, cost-effectiveness and scalable performance for billions of users. We can’t wait to see what you’ll build with them. (MetaAI, 2025)
For comparison, OpenAI’s current flagship model, GPT-5.4, and Claude 4.7 Opus both have a default context window of 1 million tokens. However, with a context window of 10 million tokens, Llama 4 Scout still exceeds these capacities tenfold, thereby enabling entirely new application scenarios, such as the simultaneous analysis of huge data archives or entire video libraries. The Llama 4 models are integrated into Meta’s AI Assistant and accessible via platforms such as WhatsApp, Messenger, Instagram, and the web.
Mistral/Mixtral by Mistral AI
The French start-up Mistral AI is now regarded as one of the world’s leading providers of efficient open-weight models. It was founded by former employees of Google and Meta, among others, and counts Microsoft among its prominent investors. Mistral has thus consolidated its position as a privacy-compliant, European alternative to the US giants. Some of Mistral’s models are also available as open source:
The models can be used and further developed free of charge. In doing so, Mistral aims to make the development of artificial intelligence more transparent and traceable. Particularly noteworthy: all data remains in Europe and is subject to the EU AI Act, meaning, among other things, that conversations with Le Chat are not transferred to US servers, as is the case with other models. This offers both enhanced data security and legal certainty for businesses and users.
The latest model is Mistral Large 3, a Frontier LLM featuring a mixture-of-experts architecture. It delivers exceptional performance on reasoning-intensive tasks and is regarded as a flagship model among efficient open-source LLMs.
In addition to the models mentioned above, Mistral AI offers Le Chat, an AI chatbot that, much like ChatGPT, can be used for entertainment, text generation and interactive applications:
What Developments Are Noticeable Outside Europe and the US?
The global LLM market is highly decentralised and very dynamic, with new providers regularly entering the market with powerful models. Regional providers are gaining prominence due to cultural specificity and extreme cost-efficiency, and several new start-ups and research institutes have released remarkable models. Examples include Cohere Command R, as well as AI4Bharat and SEA-LION, which are specifically tailored to linguistic diversity or particular tasks.
Regional Specialisation and Autonomy
Specialised ‘Sovereign AI’ projects enable independence from Western datasets. Some providers that are making their mark include the following:
- South-East Asia (SEA-LION): The model, optimised for the ASEAN region, has been developed into a powerful agent-based system by 2026 that natively understands over 1,200 regional dialects and cultural nuances.
- India (Sarvam AI & AI4Bharat): India is using AI 2026 as an infrastructure project. Sarvam AI, in collaboration with Pixxel, operates the world’s first orbital data centres to perform AI analyses (e.g. for agriculture) directly in space.
- South Korea (Samsung Gauss 2 & Upstage): Samsung has deeply integrated Gauss 2 into its hardware ecosystem; the model supports 14 languages natively on end devices (on-device), without the need for the cloud.
China as a Technological Counterweight
China has established itself as a key driver in the global LLM race, specialising as an innovator in open-weight models. 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-source, cost-effective, high-performance LLMs in emerging markets.
- Baidu (Ernie-Bot) and Alibaba (Qwen series): Two traditional tech conglomerates with their own LLM stacks and chatbot applications, often with a strong focus on the Chinese language and local market specifics.
- Z.ai (GLM family): One of the major competitors in China’s LLM ecosystem, with international branches and multiple model versions.
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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