Artificial intelligence has been readily accessible to everyone for several years now in the form of generative AI assistants and AI chatbots. More and more people and companies are regularly using these tools in both their private and professional lives.<\/p>\n\n\n\n
Providing quick answers to complex questions that Google’s (AI) search engine can only help with to a limited extent is just one of many practical applications. Research, summarising large amounts of information, text creation, editing, plagiarism checks, SEO, image generation, programming and extensive data analysis: modern solutions can support and simplify everyday (working) life in numerous ways, but also open up completely new possibilities.<\/p>\n\n\n\n
The question that almost inevitably arises is: Which AI is the best or most versatile, powerful, helpful option? ChatGPT, Gemini, DeepSeek and Claude are among the most popular platforms at the moment. So much for now: Each system takes its own approach and addresses different requirements.<\/p>\n\n\n\n
When OpenAI released ChatGPT at the end of 2022, it fundamentally changed the public perception of artificial intelligence. Within a few weeks, the tool became synonymous with generative AI. ChatGPT’s greatest strength clearly lies in its versatility. Text comprehension, analysis, programming, creative content and structured problem solving can all be combined within a single interface.<\/p>\n\n\n\n
The current GPT 5.1 model significantly expands on this approach. Compared to earlier versions, it is able to handle more complex tasks more reliably, recognise connections more precisely and deliver more comprehensible, logical results. The integrated \u2018Thinking Mode\u2019 allows the system to think through challenging questions step by step and formulate appropriately structured answers. This makes ChatGPT suitable not only for quick information, but also for strategic analyses, technical concepts or extensive data-based evaluations.<\/p>\n\n\n\n
ChatGPT can be integrated relatively flexibly into companies’ existing tech stacks via APIs, for example into CRM solutions, analysis tools or marketing platforms. The model can support processes in content creation, search engine optimisation or workflow automation. Whether it’s B2B lead generation<\/a>, content marketing<\/a> or the development of sales promotion tools<\/a>, ChatGPT always makes a valuable contribution.<\/p>\n\n\n\n Incidentally, the term \u2018GPT\u2019 stands for \u2018Generative Pre-Trained Transformer,\u2019 a special type of large language model<\/a> (LLM). This architecture forms the basis for the solution’s language comprehension and conversational interaction capabilities. The aim is to ensure communication that is as natural as possible while remaining fact-based and structured.<\/p>\n\n\n\n This mission statement is particularly evident in the latest version (5.1), which is partly a direct response to widespread criticism of GPT 5, which many users found too impersonal. The current version combines analytical depth with creative expressiveness and a human tone or warmth. In addition, the earlier and very popular GPT 4o model remains available as a legacy option, which continues to be relevant for text-heavy applications in particular.<\/p>\n\n\n\n Prices and classification:<\/p>\n\n\n\n Strengths:<\/p>\n\n\n\n Weaknesses:<\/p>\n\n\n\n Shortly after the launch of ChatGPT, Google released its own AI chatbot, Gemini, which is deeply integrated into the existing ecosystem. Originally known as Bard, the system benefited from Google’s experience in processing large amounts of data and search queries. The early phase was accompanied by errors that attracted media attention, but Gemini continued to develop positively.<\/p>\n\n\n\n The current Gemini 2.5 model covers classic use cases such as text generation, research, summaries and code support. A key advantage is its direct connection to typical Google services. Content from Google Docs, Sheets or Drive can be incorporated in a context-sensitive manner, which is particularly relevant for organisations with existing Google infrastructure.<\/p>\n\n\n\n In a business environment, this opens up promising applications in areas such as SEO<\/a>, data preparation and campaign planning. Gemini can also be very helpful for SEA<\/a>-related evaluations or content strategies within Google channels. Its close integration with existing Google workflows significantly reduces the implementation effort for relevant companies, making it easier to get started.<\/p>\n\n\n\n Gemini is also based on LLMs, i.e. large language models that are continuously retrained. Incidentally, development began back in 2021, long before the public breakthrough of generative AI.<\/p>\n\n\n\n Prices and classification:<\/p>\n\n\n\n Strengths:<\/p>\n\n\n\n Weaknesses:<\/p>\n\n\n\n DeepSeek is considered one of the most exciting developments from China in the field of generative AI. The system takes an open-source approach and is primarily aimed at technically oriented users. The relationship between performance and cost is particularly striking.<\/p>\n\n\n\n The current DeepSeek-R1 model combines fast responses with an analytical mode for complex tasks. Its strengths are particularly evident in mathematical calculations, logical reasoning and development. For developers and companies focusing on technical applications, DeepSeek thus offers a serious alternative to Western models such as ChatGPT or Gemini.<\/p>\n\n\n\n The underlying transformer architecture complies with international standards. However, the provider’s training strategy is remarkable. DeepSeek was able to develop powerful models that are hardly inferior to those of its major competitors with a significantly lower budget. According to its own figures, the training costs amounted to around 6 million US dollars, whereas for GPT 4o, for example, they were over 100 million.<\/p>\n\n\n\n Due to geopolitical restrictions, Deepseek used optimised training methods and alternative GPU solutions other than the \u2018common\u2019 high-performance processors such as NVIDIA’s A100 or H100. In a direct comparison with GPT 4o, DeepSeek-R1 showed comparable results in benchmarks for mathematical and programming tasks. However, there are limitations when it comes to linguistic and creative content.<\/p>\n\n\n\n Typical areas of application include software or web development<\/a>, code analysis, mathematical and statistical evaluations, economic data analysis and education-related applications. The open architecture allows companies to run DeepSeek locally and customise it as needed. This is, of course, a relevant advantage, especially for data protection-sensitive projects.<\/p>\n\n\n\n Prices and classification:<\/p>\n\n\n\n\n
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Gemini<\/h2>\n\n\n\n
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DeepSeek<\/h2>\n\n\n\n