Particularly in the last two years, immense technological advances have been observed in the field of artificial intelligence and natural language processing. These advances are enabling new areas of application in science, business and everyday life. The hype surrounding the use of AI was triggered in particular by the development of large language models (LLMs). This article presents some of the best-known and most powerful LLMs and explains the differences between them.
An overview of the best-known LLMs (as of January 2026)
What is an LLM and how does it work?
A large language model is a subcategory of machine learning models that are trained to understand, process, and even generate human language. In most cases, these architectures have billions of parameters, which makes the model ‘large’, i.e., comprehensive, and enables it to learn complex structures in the data. In addition, huge amounts of text are used to train the model and capture the language with all its peculiarities, such as grammar and synonyms.
The language is read in the form of ‘tokens’, i.e., the smallest units into which a text is broken down before the model processes it. The context length (e.g., ‘128k tokens’ in ChatGPT) indicates how many such units a model can keep ‘in memory’ at the same time. The cost of API usage is often billed per token.
Nowadays, it is standard for models to be ‘multimodal’. This means that they are capable of processing audio, video, and other file formats in addition to simple text. Therefore, the term ‘large language models’ is supplemented by terms such as ‘foundation model’ or ‘transformer model’, as they do not exclusively process language but are based on a broad knowledge base.
Key LLM innovations in 2026
LLMs are evolving into increasingly powerful, versatile systems and form the central building block of modern AI. Here is an overview of the key topics and developments that will be relevant for large language models in 2026:
What are the most important LLMs for 2026?
(Last updated: January 2026)
Since OpenAI released ChatGPT in November 2022, there have been significant developments in the field of large language models, with a number of well-known tech companies releasing their own models. In this section, we highlight the most important LLMs and their characteristics. The choice of provider determines the efficiency, cost, and data protection of the model.
Another key difference for companies lies in control and deployment: proprietary models such as GPT, Claude, and Gemini offer very high, stable performance, but can often only be used via APIs and allow only limited customisation. Open-source models such as LLaMA, Mistral, and Falcon, on the other hand, allow complete control over data, deployment, and data protection, but require separate hosting and more technical responsibility.
Proprietary LLM
GPT models from OpenAI
Which GPT model is the latest from OpenAI?
The current Generative Pretrained Transformer (GPT) model, released by OpenAI in December 2025, is GPT-5.2. The model follows the approach of a unified system that dynamically decides how much computing and reasoning depth to apply depending on the request. In addition, the accuracy of the model has been improved compared to its predecessors, as new functions are available to manage what the model ‘knows’ and ‘remembers’.
What GPT-5 versions are available?
GPT-5.2 Instant, Thinking and Pro are available in the ChatGPT application with paid plans, and are available to all developers in the API. All three ChatGPT models (Instant, Thinking and Pro) have a new knowledge base set to August 2025. (OpenAI, 2025). Other models include gpt-5.2-codex, optimised for agent-based coding tasks; gpt-5-mini for cost-optimised logical reasoning and chat; and gpt-5-nano for high-throughput tasks and simple instructions. GPT-5 is described by OpenAI as the ‘best general-purpose model’ for general and agent-based tasks (OpenAI, 2026). We have compiled more detailed information on OpenAI's current GPT model in our in-depth article on GPT-5 here.
What are the previous models of GPT?
Earlier versions of GPT will remain available to users as a legacy option for a limited time. Unlike GPT-5, its predecessor GPT-4.1 is a non-reasoning-oriented model with a very large context window, optimised for instructions, tool calls and extensive inputs. The mini model GPT-4o mini has a smaller architecture with fewer parameters. This is particularly advantageous for real-time applications where response time is more important and compromises in performance can be made. o3 / o3-pro is currently the most powerful dedicated reasoning model in the o-series, a special advancement within LLMs as an inference model, designed to solve complex, multi-step analysis tasks.
We answer the most important questions about OpenAI's ChatGPT as an application of the models in a separate article.
Claude models from Anthropic
The model variants follow a hierarchical structure with different sizes and performance classes:
The company Anthropic, which was founded in 2021 by several former developers from OpenAI, the company behind ChatGPT, is a significant competitor with its Claude series. The latest model performs on par with, or in some cases better than, the GPT-4 and GPT-4o models in benchmarks. Claude is characterised by its focus on security, ethical control, and so-called ‘constitutional AI’ principles, i.e., the behaviour of the model is guided by a set of guiding principles to provide reliable answers. This makes it particularly relevant for use in context-sensitive enterprise applications. In addition, large context windows, multimodality, and improved agent capabilities are core features, e.g., the autonomous execution of multi-step tasks.
The variants are part of the Claude 4.5 family, which was released at the end of 2025. 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 and integrations with partner clouds (e.g. Google Cloud Vertex AI, AWS Bedrock).
Google and Gemini
In 2026, Google is one of the leading providers of multimodal large language models with its Gemini series. It has evolved from a pure chatbot to a personalised, agentic system that is deeply embedded in the Google ecosystem and integrated into third-party services. Current models are as follows:
The new version of Google LLM is Gemini-3, which was officially released in November 2025. The Gemini-3 family focuses on deep reasoning and agentic functions and has more advanced thinking and multimodal capabilities that replace earlier models. Gemini 3 Pro and Deep Think are the most advanced model variants, while Gemini 3 Flash and its predecessor 2.5 Flash, are designed for speed and efficiency.
A detailed classification of the individual versions and development stages can be found in our article "Google Gemini Explained: Overview of 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
Google addresses both complex scientific and technical tasks as well as interactive applications in education and research, as the models feature large context windows of millions of tokens, multimodality and an efficient mixture-of-experts architecture. This enables the analysis of long documents, code bases or multimedia collections in a single pass. Google DeepMind developed the Gemini series as the successor to the LaMDA and PaLM models.
Grok AI
Grok AI is the LLM family from 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 provide more empathetic, emotionally understandable responses and improve its conversational quality. 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
The special feature of Grok is its close integration with dynamic information sources and platforms, which means that the model performs particularly well with current topics, research-like queries and interactive conversations. The model is a relevant alternative to established models for use cases such as real-time assistance, creative text work, or rapid information processing. Grok 4.1 is available via web, X, and mobile apps, and the models can be selected in different modes (‘Auto’ or explicitly ‘Grok 4.1’). However, Grok continues to be accompanied by various controversies: the latest criticism in early 2026 was due to problematic outputs and insufficient content moderation, especially with regard to sensitive or even pornographic content. This reinforces the fact that issues such as content control and governance are key areas of development for further 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 introduced its LLM MetaAI, LLaMA. Since the 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, i.e. they natively support text and image inputs and are designed for multimodal tasks. They are also multilingual. Meta develops the models openly and makes weights, model cards and developer documentation publicly available for research, product integration and generative AI applications.
"We have optimised our models for easy deployment, cost efficiency and scalable performance for billions of users. We are excited to see what you will develop with them." – MetaAI, 2025
For comparison, OpenAI's GPT-4o has a context window of 128,000 tokens and Claude 3 Opus has 200,000 tokens. With a context window of 10 million tokens, Llama 4 Scout significantly exceeds these orders of magnitude, opening up new application possibilities. The Llama 4 models are integrated into Meta's AI Assistant and accessible via platforms such as WhatsApp, Messenger, Instagram and the web. Previous versions of the Llama family are Llama 3.1, 3.2 and 3.3, as well as the older versions 2 and 1, which were already suitable for simple to medium generative AI tasks and were used in research and development projects.
Mistral/Mixtral by Mistral AI
Mistral AI is a French start-up specialising in the development of powerful large language models. It was founded by former Google and Meta employees, among others, and has well-known investors such as Microsoft. Compared to the major providers, some of Mistral's models are also available as open source:
The models can be used free of charge and freely developed further. Mistral's goal is to make the development of artificial intelligence more transparent and comprehensible. Particularly noteworthy: all data remains in Europe and is subject to the EU AI Act, meaning that conversations with Le Chat, for example, are not transferred to US servers, as is the case with other models. This offers both increased data security and legal reliability for companies and users.
The latest model is Mistral Large 3, the frontier LLM with a mixture-of-experts architecture. It offers very high performance in reasoning-intensive tasks and is considered a showcase model for efficient open-source LLMs.
In addition to the models mentioned above, Mistral AI offers Le Chat, an AI chatbot that, similar to ChatGPT, can be used for entertainment, text generation, and interactive applications:
What developments are taking place outside Europe and the US?
The models and updates mentioned are only a small part of the ever-growing LLM market. This market is very dynamic, with new providers offering powerful models appearing regularly. Several new start-ups and research institutes have also released remarkable models, including Cohere Command R and regional developments such as AI4Bharat and SEA-LION, which are specifically tailored to language diversity or special tasks.
In the public perception, much of the AI development in the field of large language models is concentrated in Europe and heavily focused on the US, as large, established companies and extensive data sets are available there. As a result, Western models often have limited responsiveness to languages and cultures in other regions. For example, Llama-2 training data contains only about 0.5% content from Southeast Asian countries, even though over 1,200 dialects and languages are spoken in this region (Carnegie, 2025).
The SEA-LION model was therefore introduced in 2024 as the first large language model specifically trained for the ASEAN region. Although it is only a fraction of the size of GPT-4, for example, it can be more helpful in specific applications, such as customer support, as it can respond more specifically to the cultural differences of individual countries.
China is currently a key driver in the global LLM race. Several large technology companies and start-ups are building their own large language models, including:
- DeepSeek, a Chinese AI start-up from Hangzhou, with the DeepSeek-R1, V3 and other models as open-weight, cost-effective, powerful LLMs in emerging markets
- Baidu (Ernie Bot) and Alibaba (Qwen series) are two traditional tech companies with their own LLM stacks and chatbot applications, often with strong Chinese language and country specificity
- Z.ai (GLM family) is one of the biggest competitors in China's LLM ecosystem, with international offices and several model versions (e.g. GLM-4.7).
Other international players include South Korea with the Solar Pro 2 model from the start-up Upstage, which achieves competitive performance despite significantly lower parameter numbers. India has an emerging LLM scene and is primarily developing language-focused projects such as Sarvam AI.
2026 will also see a trend towards regionally specialised LLMs worldwide, a development that will make large language models more accessible and usable for regions that have been underrepresented in the global AI market to date. Key trends include end-to-end multimodality (text, image, audio, video), ultra-long context windows, and the increasing importance of open-source models.
LLM development and use in customer communication
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 focuses on complex reasoning, agent and enterprise capabilities. At the same time, open-source models, such as those from Meta, Mistral and Asian providers, are coming to the fore and offering realistic alternatives for companies. Governance, data protection and regulatory requirements play a central role here, especially in Europe. Companies must embed LLMs not only as technology, but as a strategic building block in their processes and data landscapes.
Despite the impressive capabilities of LLMs, practical experience shows that they cannot always be used optimally for direct customer communication.
They often provide generalised responses, integration into existing systems (e.g. CRM or ticketing) is problematic, or they require extensive customisation to take brand identity and industry-specific knowledge into account. Specialised solutions, such as moinAI, usually ensure faster, more accurate and GDPR-compliant customer interactions.
Discover how moinAI supports your company in the smart automation of customer enquiries and why it is the ideal partner for professional customer communication.
[[CTA headline="Large language models such as ChatGPT are not designed for customer communication!" subline="Discover moinAI, a smart automation solution developed specifically for your customer communication." button="Try it now"]]
.svg)
