Comparative analysis of the leading AI models: Google Gemini 2.0, Deepseek R2 and GPT-4.5 from Openaai
Xpert pre-release
Language selection 📢
Published on: March 24, 2025 / update from: March 24, 2025 - Author: Konrad Wolfenstein
Comparative analysis of the leading AI models: Gemini 2.0, Deepseek and GPT-4.5-Image: Xpert.digital
A detailed view of the current landscape of generative artificial intelligence (reading time: 39 min / no advertising / no paywall)
The rise of the intelligent machines
We are in an era of unprecedented progress in the field of artificial intelligence (AI). The development of large voice models (LLMS) has reached a speed in recent years that surprised many experts and observers. These highly developed AI systems are no longer just tools for specialized applications; They penetrate more and more areas of our lives and change the way we work, communicate and understand the world around us.
At the top of this technological revolution are three models that cause a stir in the professional world and beyond: Gemini 2.0 by Google Deepmind, Deepseek from Deepseek Ai and GPT-4.5 from Openaai. These models represent the current state of the art in AI research and development. They demonstrate impressive skills in a variety of disciplines, from the processing of natural language to generation of computer code to complex logical thinking and creative content creating.
This report makes a comprehensive and comparative analysis of these three models in order to examine their respective strengths, weaknesses and areas of application in detail. The aim is to create a profound understanding of the differences and similarities of these state-of-the-art AI systems and to offer an informed basis for evaluating your potential and limitations. We will not only examine the technical specifications and performance data, but also the underlying philosophical and strategic approaches of the developers who have shaped these models.
Suitable for:
The dynamics of the AI competition: a three-way battle of the giants
The competition for dominance in the field of AI is intense and is dominated by a few but very influential actors. Google Deepmind, Deepseek Ai and Openai are not just technology companies; They are also research institutions that are on the foremost front of the AI innovation. Your models are not only products, but also manifestations of their respective visions from the future of AI and its role in society.
Google Deepmind, with its deep roots in research and its immense computing power, follows Gemini 2.0 an approach of versatility and multimodality. The company sees the future of AI in intelligent agents who are able to cope with complex tasks in the real world and to seamlessly process and generate various types of information - text, images, audio, video -.
Deepseek Ai, an emerging company based in China, has made a name for itself with Deepseek, which is characterized by its remarkable efficiency, its strong recourse skills and its commitment to open source. Deepseek positions itself as a challenger in the AI market, which offers a powerful and at the same time accessible alternative to the models of the established giants.
Openaai, known by Chatgpt and the GPT model family, has again set a milestone in the development of conversational AI with GPT-4.5. Openai focuses on creating models that are not only intelligent, but also intuitive, empathetic and able to interact with people on a deeper level. GPT-4.5 embodies this vision and aims to move the limits of what is possible in human-machine communication.
Gemini 2.0: A family of AI models for the age of agents
Gemini 2.0 is not just a single model, but an entire family of AI systems developed by Google Deepmind to meet the diverse requirements of the modern AI ecosystem. This family includes various variants, each tailored to specific areas of application and performance requirements.
Suitable for:
Recent developments and announcements (as of March 2025): The Gemini family is growing
In the course of 2025, Google Deepmind continuously presented new members of the Gemini 2.0 family and thus underlined its ambitions in the AI market. Particularly noteworthy is the general availability of Gemini 2.0 Flash and Gemini 2.0 Flash-Lite, which are positioned as powerful and cost-efficient options for developers.
Gemini 2.0 Flash himself describes Google as a “work animal” model. This name indicates its strengths in terms of speed, reliability and versatility. It is designed to deliver high performance with low latency, which makes it ideal for applications in which fast response times are decisive, such as: B. Chatbots, real-time translations or interactive applications.
Gemini 2.0 Flash-Lite, on the other hand, aims at maximum cost efficiency. This model is optimized for applications with high throughput, in which low operating costs per request, e.g. B. in the mass processing of text data, the automatic content moderation or the provision of AI services in resource-limited environments.
In addition to these generally available models, Google has also announced experimental versions such as Gemini 2.0 Pro and Gemini 2.0 Flash Thinking Experimental. These models are still in development and serve to explore the limits of possible in AI research and to obtain feedback from developers and researchers at an early stage.
Gemini 2.0 Pro is highlighted as the most powerful model of the family, especially in the areas of coding and world knowledge. A remarkable feature is its extremely long context window of 2 million tokens. This means that Gemini 2.0 Pro is able to process extremely large amounts of text and understand what it makes ideal for tasks that require a deep understanding of complex relationships, such as: B. the analysis of extensive documentation, answering complex questions or generation of code for large software projects.
Gemini 2.0 Flash Thinking Experimental, on the other hand, focuses on improving the reasoning skills. This model is able to explicitly present its thinking process to improve performance and increase the explanability of the AI decisions. This function is particularly important in areas of application in which transparency and traceability of the AI decisions are of crucial importance, such as: B. in medicine, finance or in case law.
Another important aspect of recent developments in Gemini 2.0 is the setting of older models of the Gemini 1.X series and the Palm and Codey models by Google. The company strongly recommends that users of these older models to migrate to Gemini 2.0 Flash to avoid service interruptions. This measure indicates that Google is convinced of the progress in the architecture and performance of the Gemini 2.0 generation and wants to position it as the future platform for its AI services.
The global range of Gemini 2.0 Flash is underlined by its availability via the Gemini web application in more than 40 languages and over 230 countries and areas. This is shown by Google's commitment to democratizing access to advanced AI technology and its vision of a AI that is accessible and usable for people around the world.
Architectural overview and technological foundations: Multimodality and agent functions in focus
The Gemini 2.0 family was designed from the ground up for the "agent age". This means that the models are not only designed to understand and generate text, but are also able to interact with the real world, use tools, to generate and create and generate images. These multimodal skills and agent functions are the result of a profound architectural focus on the needs of future AI applications.
The different variants of Gemini 2.0 are geared towards different focal points in order to cover a wide range of applications. Gemini 2.0 Flash is designed as a versatile model with low latency, which is suitable for a wide range of tasks. Gemini 2.0 Pro, on the other hand, specializes in coding, world knowledge and long contexts and is aimed at users who need the highest performance in these areas. Gemini 2.0 Flash-Lite is intended for cost-optimized applications and offers a balance between performance and economy. Gemini 2.0 Flash Thinking Experimental finally aims at improved Reasoning skills and researches new ways to improve the logical thinking processes of AI models.
A central feature of the Gemini 2.0 architecture is the support of multimodal inputs. The models can process text, code, images, audio and video as input and thus integrate information from different sensory modalities. The output can also be done multimodal, whereby Gemini 2.0 can generate text, images and audio. Some output modalities, such as B. Video, are currently still in the private preview phase and will probably be generally available in the future.
The impressive performance of Gemini 2.0 is also due to Google's investments in special hardware. The company relies on its own trillium TPUS (Tensor Processing Units), which were specially developed for the acceleration of AI calculations. This tailor-made hardware enables Google to train and operate its AI models more efficiently and thus achieve a competitive advantage in the AI market.
The architectural orientation of Gemini 2.0 to multimodality and the enabling of AI agents who can interact with the real world is an essential distinction feature compared to other AI models. The existence of different variants within the Gemini 2.0 family indicates a modular approach that enables Google to adapt the models flexibly to specific performance or cost requirements. The use of his own hardware underlines Google's long-term commitment to the further development of the AI infrastructure and its determination to play a leading role in the AI age.
Training data: scope, sources and the art of learning
Although detailed information about the exact scope and the composition of the training data for Gemini 2.0 is not open to the public, it can be derived from the skills of the model that it was trained on massive data records. These data records probably include terabytes or even petabytes of text and coded data as well as multimodal data for the 2.0 versions that contain images, audio and video.
Google has an invaluable data treasure that comes from the entire spectrum of the Internet, digitized books, scientific publications, news articles, social media contributions and countless other sources. This huge amount of data forms the basis for training the Google AI models. It can be assumed that Google uses sophisticated methods to ensure the quality and relevance of the training data and to filter potential distortions or unwanted content.
The multimodal skills of Gemini 2.0 require the inclusion of image, audio and video data into the training process. This data probably comes from various sources, including publicly available image databases, audio archives, video platforms and possibly also proprietary data records from Google. The challenge of multimodal data acquisition and processing is to integrate the different data modalities sensibly and to ensure that the model learns the connections and relationships between them.
The training process for large voice models such as Gemini 2.0 is extremely calculated and requires the use of powerful supercomputers and specialized AI hardware. It is an iterative process in which the model is repeatedly fed with the training data and its parameters are adapted so that it fulfills the desired tasks. This process can take weeks or even months and requires a deep understanding of the underlying algorithms and the subtleties of machine learning.
Most important skills and diverse applications: Gemini 2.0 in action
Gemini 2.0 Flash, Pro and Flash-Lite offer an impressive range of skills that make you suitable for a variety of applications in various industries and areas. The most important functions include:
Multimodal insert and output
Processing and generating the ability to process and generate text, code, images, images, audio and video, opens up new opportunities for human-machine interaction and the creation of multimodal content.
Tool use
Gemini 2.0 can use external tools and APIs to access information, carry out actions and manage complex tasks. This enables the model to go beyond its own skills and to adapt in dynamic environments.
Long context window
In particular, Gemini 2.0 Pro with its 2 million token context window can process and understand extremely long texts and understand what tasks such as the analysis of extensive documents or the summary of long conversations predestines.
Improved Reasoning
The experimental version Gemini 2.0 Flash Thinking Experimental aims to improve the logical thinking processes of the model and enable it to solve more complex problems and make rational decisions.
Coding
Gemini 2.0 Pro is particularly strong in the coding and can generate high -quality code in various programming languages, recognize and fix errors in the code and support them in software development.
Function Calling
The ability to call up functions enables Gemini 2.0 to interact with other systems and applications and to automate complex work processes.
Gemini 2.0's potential applications are almost limitless. Some examples include:
Creation of content
Generation of texts, articles, blog posts, scripts, poems, music and other creative content in various formats and styles.
automation
Automation of routine tasks, data analysis, process optimization, customer service and other business processes.
Coding support
Support of software developers in the codegenization, error correction, code documentation and learning new programming languages.
Improved viewfinder experiences
More intelligent and more context-related search results that go beyond traditional keyword search and help users to answer complex questions and gain deeper insights into information.
Business and corporate applications
Use in areas such as marketing, sales, human resources, finance, legal and healthcare to improve efficiency, decision -making and customer satisfaction.
Gemini 2.0: Transformative AI agent for everyday life and work
Specific projects such as Project Astra, which researches the future skills of a universal AI assistant, and Project Mariner, a prototype for browser automation, demonstrate the practical possible uses of Gemini 2.0. These projects show that Google sees Gemini technology not only as a tool for individual tasks, but as a basis for the development of extensive AI solutions that are able to support people in their everyday life and in their professional activities.
The versatility of the Gemini 2.0 model family enables their use in a broad spectrum of tasks, from general applications to specialized areas such as coding and complex reasoning. The focus on agent functions indicates a trend towards more proactive and helpful AI systems, which not only react to commands, but are also able to act independently and solve problems.
Suitable for:
Availability and accessibility for users and developers: AI for everyone
Google is actively trying to make Gemini 2.0 accessible to both developers and end users. Gemini 2.0 Flash and Flash-Lite are available via the Gemini API in Google Ai Studio and Vertex AI. Google AI Studio is a web-based development environment that enables developers to experiment with Gemini 2.0, create prototypes and to develop AI applications. Vertex AI is Google's cloud platform for machine learning, which offers a comprehensive suite of tools and services for training, provision and management of AI models.
The experimental version Gemini 2.0 Pro is also accessible in Vertex AI, but is more aimed at advanced users and researchers who want to explore the latest functions and possibilities of the model.
A version of Gemini 2.0 Flash Experimental optimized for the chat is available in the Gemini web application and the mobile app. This also enables end users to experience Gemini 2.0's skills in a conversational context and to give feedback that contributes to the further development of the model.
Gemini is also integrated into Google Workspace applications such as Gmail, Docs, Sheets and Slides. This integration enables users to use the AI functions of Gemini 2.0 directly in their daily work processes, e.g. B. when writing emails, creating documents, analyzing data in spreadsheet or creating presentations.
The staggered availability of Gemini 2.0, from experimental versions to generally available models, enables a controlled introduction and the collection of user feedback. This is an important aspect of the Google strategy to ensure that the models are stable, reliable and user-friendly before they are made accessible to a wide audience. Integration into widespread platforms such as Google Workspace facilitates the use of the model's skills through a broad user base and contributes to integrating AI into the everyday life of people.
Well -known strengths and weaknesses: an honest view of Gemini 2.0
Gemini 2.0 received a lot of praise for his impressive skills in the AI community and in the first user tests. The reported strengths include:
Improved multimodal skills
Gemini 2.0 exceeds its predecessors and many other models in the processing and generation of multimodal data, which predestines it for a variety of applications in the areas of media, communication and creative industries.
Faster workmanship
Gemini 2.0 Flash and Flash-Lite are optimized for speed and offer low latency, which makes it ideal for real-time applications and interactive systems.
Improved reasoning and context understanding
Gemini 2.0 shows progress in logical thinking and in the understanding of complex contexts, which leads to more precise and relevant answers and results.
Strong performance in the coding and processing of long contexts
In particular, Gemini 2.0 Pro impresses with his skills in codegenization and analysis as well as its extremely long context window, which enables him to process extensive amounts of text.
Despite these impressive strengths, there are also areas in which Gemini 2.0 still has improvement potential. The reported weaknesses include:
Potential distortions
Like many large voice models, Gemini 2.0 can reflect distortions in his training data, which can lead to biased or discriminatory results. Google is actively working on recognizing and minimizing these distortions.
Restrictions on the complex problem solving in real time
Although Gemini 2.0 shows progress in the Reasoning, it can still reach its limits with very complex problems in real time, especially compared to specialized models that are optimized for certain types of Reasoning tasks.
There is a need for improvement in composition tool in Gmail
Some users have reported that the composition tool in Gmail, which is based on Gemini 2.0, is not yet perfect in all aspects and has potential for improvement, e.g. B. with regard to the stylistic consistency or the consideration of specific user preferences.
Compared to competitors such as GROK and GPT-4, Gemini 2.0 shows strengths in multimodal tasks, but could lag behind in certain Reasoning benchmarks. It is important to emphasize that the AI market is very dynamic and the relative performance of the different models is constantly changing.
Overall, Gemini 2.0 offers impressive skills and represents significant progress in the development of large language models. Like other LLMs, however, it also faces challenges in relation to distortions and consistent reasoning across all tasks. However, the continuous further development and improvement of Gemini 2.0 by Google Deepmind will probably continue to minimize these weaknesses in the future and expand its strengths.
Results of relevant benchmarks and performance comparisons: numbers speak volumes
Benchmark data show that Gemini 2.0 Flash and Pro in various established benchmarks such as MMLU (massive multitask Language Understanding), Livecodebech, Bird-SQL, GPQA (Graduate-Level Google-Proof Q & A), Math, Hiddenmath, Global MMLU, MMMU (Massive Multi-Discipline Muldodal Understanding), Covost2 (Conversational Voice to Speech Translation) and Egososchema have a significant increase in performance towards their predecessors.
The different variants of Gemini 2.0 show different strengths, whereby Pro usually performs better for more complex tasks, while flash and flash lite are optimized for speed and cost efficiency.
Compared to models of other companies such as GPT-4O and Deepseek, the relative performance varies depending on the specific benchmark and the compared models. For example, Gemini 2.0 exceeds Flash 1.5 Pro in important benchmarks and is twice as fast at the same time. This underlines the increases in efficiency that Google has achieved through the further development of the Gemini architecture.
Gemini 2.0 Pro achieves higher values than Gemini 1.5 pro These improvements are particularly relevant for software developers and companies that use AI for codegenization and analysis.
In mathematics benchmarks such as Math and Hiddenmath, the 2.0 models also show significant improvements to their predecessors. This indicates that Google has made progress in improving Gemini 2.0's reasoning skills, especially in areas that require logical thinking and mathematical understanding.
However, it is important to note that benchmark results are only part of the overall picture. The actual performance of a AI model in real applications can vary depending on the specific requirements and the context. Nevertheless, benchmark data provide valuable insights into the relative strengths and weaknesses of the different models and enable an objective comparison of their performance.
🎯🎯🎯 Benefit from Xpert.Digital's extensive, fivefold expertise in a comprehensive service package | R&D, XR, PR & SEM
AI & XR 3D Rendering Machine: Fivefold expertise from Xpert.Digital in a comprehensive service package, R&D XR, PR & SEM - Image: Xpert.Digital
Xpert.Digital has in-depth knowledge of various industries. This allows us to develop tailor-made strategies that are tailored precisely to the requirements and challenges of your specific market segment. By continually analyzing market trends and following industry developments, we can act with foresight and offer innovative solutions. Through the combination of experience and knowledge, we generate added value and give our customers a decisive competitive advantage.
More about it here:
Inexpensive AI leaders: Deepseek R2 Vs. AI giant-a powerful alternative
Inexpensive AI leaders: Deepseek vs. Ai giant-a powerful alternative-Image: Xpert.digital
Deepseek: The efficient challenger with a focus on Reasoning and Open Source
Deepseek is a AI model developed by Deepseek Ai and is characterized by its remarkable efficiency, its strong reasoning skills and its commitment to open source. Deepseek positions itself as a powerful and inexpensive alternative to the models of the established AI giants and has already attracted a lot of attention in the AI community.
Architectural framework and technical specifications: efficiency through innovation
Deepseek uses a modified transformer architecture that relies on efficiency through Grouped Query Attention (GQA) and dynamic savings activation (Mixture of Experts-Moe). These architectural innovations enable Deepseek to achieve high performance with comparatively low arithmetic resources.
The Deepseek-R1 model, the first publicly available version of Deepseek, has 671 billion parameters, but only 37 billion per token is activated. This approach of the “Sparse Activation” significantly reduces the computing costs during the inference, since only a small part of the model is active for each input.
Another important architectural feature of Deepseek is the Multi-Head Latent Attention (MLA) mechanism. MLA optimizes the attention mechanism, which is a central component of the transformer architecture, and improves the efficiency of information processing in the model.
The focus of Deepseek is on the balance between performance and practical restrictions on the operational restrictions, especially in the areas of codegenization and multilingual support. The model is designed to deliver excellent results in these areas and at the same time be inexpensive and resource -saving.
The MOE architecture, which Deepseek uses, divides the AI model into separate subnetworks, each of which specializes in a subset of the input data. During the training and the inference, only a part of the subnetworks is activated for each input, which significantly reduces the computing costs. This approach enables Deepseek to train and operate a very large model with many parameters without excessively increasing the inference speed or costs.
Findings on training data: Quality before quantity and the value of specialization
Deepseek attaches great importance to domain -specific training data, especially for coding and Chinese language. The company is convinced that the quality and relevance of the training data are more important for the performance of a AI model than the pure quantity.
The Deepseek-V3 training body comprises 14.8 trillion tokens. A significant part of this data comes from domain -specific sources that focus on coding and Chinese language. This enables Deepseek to perform particularly strong services in these areas.
The training methods from Deepseek includes Reinforcement Learning (RL), including the unique pure-RL approach for deepseek-r1-zero and the use of cold start data for deepseek-r1. Reinforcement Learning is a method of machine learning, in which an agent learns to act in an environment by receiving rewards for desired actions and punishments for unwanted actions.
Deepseek-R1-Zero was trained without an initial supervised fin tuning (SFT) to promote Reasoning skills purely through RL. Supervized Fine-Tuning is a usual technology in which a pre-trained language model with a smaller, annotated data set is finished in order to improve its performance in certain tasks. However, Deepseek has shown that it is possible to achieve strong recurrence skills even without SFT by Reinforcement Learning.
Deepseek-R1, on the other hand, integrates cold start data in front of the RL to create a strong foundation for readering and non-reading tasks. Cold start data is data used at the beginning of the training to convey a fundamental understanding of the language and the world to the model. With the combination of cold start data with Reinforcement Learning, Deepseek can train a model that has strong reasoning skills and a wide general knowledge.
Advanced techniques such as Group Relative Policy Optimization (GRPO) are also used to optimize the RL training process and to improve the stability and efficiency of the training.
Suitable for:
Core skills and potential applications: Deepseek in action
Deepseek-R1 is characterized by a number of core skills that predestine it for various applications:
Strong Reasoning capabilities
Deepseek-R1 is particularly strong in logical thinking and in problem solving, especially in areas such as mathematics and coding.
Superior performance in coding and mathematics
Benchmark data show that Deepseek-R1 often cuts better in coding and mathematics benchmarks than many other models, including some models from Openaai.
Multilingual support
Deepseek-R1 offers support for several languages, which makes it attractive for global applications and multilingual users.
Cost efficiency
The efficient architecture of Deepseek-R1 enables the model to operate with comparatively small computing costs, which makes it an inexpensive option for companies and developers.
Open source availability
Deepseek Ai is committed to the open source idea and provides many of its models, including Deepseek LLM and deepseek code, as open source. This promotes transparency, cooperation and further development of AI technology by the community.
Potential applications for Deepseek-R1 include:
Content creation
Generation of technical texts, documentation, reports and other content that require a high degree of accuracy and detail.
AI tutor
Use as an intelligent tutor in the areas of mathematics, computer science and other technical disciplines to support learners in problem solving and understanding complex concepts.
Development tools
Integration in development environments and tools to support software developers in codegen, troubleshooting, code analysis and optimization.
Architecture and urban planning
Deepseek Ai is also used in architecture and urban planning, including the processing of GIS data and the code of codenization for visualizations. This shows the potential of Deepseek to create added value even in specialized and complex areas of application.
Deepseek-R1 can solve complex problems by disassembling them in individual steps and making the thinking process transparent. This ability is particularly valuable in areas of application in which traceability and explanability of the AI decisions are important.
Availability and licensing options: Open source for innovation and accessibility
Deepseek relies strongly on open source and has published several of its models under open source licenses. Deepseek LLM and Deepseek code are available as open source and can be freely used, modified and developed by the community.
Deepseek-R1 is published under the co-license, a very liberal open source license that allows commercial and non-commercial use, modification and further distribution of the model. This open source strategy distinguishes Deepseek from many other AI companies that usually keep their models proprietary.
Deepseek-R1 is available on various platforms, including Hugging Face, Azure Ai Foundry, Amazon Dark and IBM Watsonx.ai. Hugging Face is a popular platform for publication and exchange of AI models and data records. Azure Ai Foundry, Amazon Dark and IBM Watsonx.ai are cloud platforms that enable access to Deepseek-R1 and other AI models via APIs.
The models from Deepseek are known as inexpensive compared to competitors, both in terms of training and inference costs. This is an important advantage for companies and developers who want to integrate AI technology into their products and services, but have to pay attention to their budgets.
The engagement of Deepseek for open source and cost efficiency makes it an attractive option for a wide range of users, from researchers and developers to companies and organizations. The open source availability promotes transparency, cooperation and faster further development of deepseek technology by the AI community.
Suitable for:
Reported strengths and weaknesses: a critical look at Deepseek
Deepseek has received a lot of recognition in the AI community for its strengths in the areas of coding, mathematics and reasoning. The reported strengths include:
Superior performance in coding and mathematics
Benchmark data and independent reviews confirm the outstanding performance of deepseek-r1 in coding and mathematics benchmarks, often better than those of Openai models.
Cost efficiency
The efficient architecture of Deepseek-R1 enables the model to operate with lower computing costs than many other comparable models.
Open source availability
The open source licensing of deepseek models promotes transparency, collaboration and innovation in the AI community.
Strong Reasoning capabilities
Deepseek-R1 shows impressive skills in logical thinking and problem solving, especially in technical domains.
Despite these strengths, there are also areas in which Deepseek still has improvement potential. The reported weaknesses include:
Potential distortions
Like all major voice models, Deepseek can reflect distortions in its training data, even though Deepseek ani tries to minimize them.
Smaller ecosystem compared to established providers
Deepseek is a relatively young company and does not yet have the same extensive ecosystem of tools, services and community resources such as established providers such as Google or Openaai.
Limited multimodal support beyond text and code
Deepseek primarily focuses on text and code processing and currently does not offer comprehensive multimodal support for images, audio and video such as Gemini 2.0.
Continues to need human supervision
Although Deepseek-R1 performs impressive performance in many areas, human supervision and validation are still required in critical use cases to avoid mistakes or unwanted results.
Occasional hallucinations
Like all major language models, Deepseek can occasionally produce hallucinations, i.e. generate incorrect or irrelevant information.
Dependence on large arithmetic resources
The training and operation of Deepseek-R1 require significant arithmetic resources, although the efficient architecture of the model reduces these requirements compared to other models.
Overall, Deepseek is a promising AI model with special strengths in the areas of coding, mathematics and reasoning. Its cost efficiency and open source availability make it an attractive option for many users. The further development of Deepseek by Deepseek Ai is expected to continue to minimize its weaknesses in the future and expand its strengths.
Results of relevant benchmarks and performance comparisons: Deepseek in comparison
Benchmark data show that Deepseek-R1 can keep up with Openai-O1 in many Reasoning benchmarks or even surpass them, especially in mathematics and coding. Openai-O1 refers to earlier models from Openai, which were published before GPT-4.5 and in certain areas, such as: B. Reasoning, possibly still competitive.
In mathematics benchmarks such as Aime 2024 (American Invitational Mathematics Examination) and Math-500, Deepseek-R1 achieves high values and often exceeds OpenAi models. This underlines the strengths of Deepseek in mathematical reasoning and problem solving.
In the area of coding, Deepseek-R1 also shows strong services in benchmarks such as Livecodebech and Codeforces. Livecodebench is a benchmark for code furniture, while Codeforces is a platform for programming competitions. The good results of deepseek-r1 in these benchmarks indicate its ability to generate high-quality code and to solve complex programming tasks.
In general knowledge benchmarks such as GPQA Diamond (Graduate level Google Proof Q & a), Deepseek-R1 is often at eye level or slightly under Openai-O1. GPQA Diamond is a demanding benchmark that tests the general knowledge and the reasoning assets of AI models. The results indicate that Deepseek-R1 is also competitive in this area, although it may not quite achieve the same performance as specialized models.
The distilled versions of Deepseek-R1, which are based on smaller models such as Llama and Qwen, also show impressive results in various benchmarks and in some cases even surpass Openai-o1-mini. Distillation is a technique in which a smaller model is trained to imitate the behavior of a larger model. The distilled versions of Deepseek-R1 show that the core technology of Deepseek can also be used effectively in smaller models, which underlines its versatility and scalability.
Our recommendation: 🌍 Limitless reach 🔗 Networked 🌐 Multilingual 💪 Strong sales: 💡 Authentic with strategy 🚀 Innovation meets 🧠 Intuition
From local to global: SMEs conquer the global market with clever strategies - Image: Xpert.Digital
At a time when a company's digital presence determines its success, the challenge is how to make this presence authentic, individual and far-reaching. Xpert.Digital offers an innovative solution that positions itself as an intersection between an industry hub, a blog and a brand ambassador. It combines the advantages of communication and sales channels in a single platform and enables publication in 18 different languages. The cooperation with partner portals and the possibility of publishing articles on Google News and a press distribution list with around 8,000 journalists and readers maximize the reach and visibility of the content. This represents an essential factor in external sales & marketing (SMarketing).
More about it here:
Facts, intuition, empathy: that makes GPT-4.5 so special
GPT-4.5: Conversational excellence and the focus on natural interaction-Image: Xpert.digital
GPT-4.5: Conversational excellence and the focus on natural interaction
GPT-4.5, with the code name “Orion”, is the latest flagship model from Openaai and embodies the company's vision of a AI that is not only intelligent, but also intuitive, empathetic and able to interact with people on a deep level. GPT-4.5 primarily focuses on improving the conversation experience, increasing the correction of facts and reducing hallucinations.
Current specifications and main features (as of March 2025): GPT-4.5 unveiled
GPT-4.5 was published as a Research Preview in February 2025 and is called the “largest and best model for chat” so far. This statement underlines the primary focus of the model on conversational skills and the optimization of human-machine interaction.
The model has a context window of 128,000 tokens and a maximum output length of 16,384 tokens. The context window is smaller than that of Gemini 2.0 Pro, but still very large and enables GPT-4.5 to have longer discussions and to process more complex inquiries. The maximum output length limits the length of the answers that the model can generate.
The state of knowledge of GPT-4.5 ranges until September 2023. This means that the model has information and events up to this point, but has no knowledge of later developments. This is an important restriction that must be taken into account when using GPT-4.5 for time-critical or current information.
GPT-4.5 Integrates Functions such as web search, file and image uploads as well as the Canvas tool in Chatgpt. The model enables the model to access current information from the Internet and to enrich its answers with current knowledge. File and image uploads enable users to provide the model additional information in the form of files or images. The Canvas tool is an interactive drawing board that enables users to integrate visual elements into their conversations with GPT-4.5.
Unlike models such as O1 and O3-Mini, which concentrate on step-by-step reasoning, GPT-4.5 scales up the unsupervised learning. Unsupervised Learning is a method of machine learning, in which the model learns from unannoted data, without explicit instructions or labels. This approach aims to make the model more intuitive and more talked, but may be able to pay the performance with complex problem -solving tasks.
Architectural design and innovations: scaling and alignment for conversation
GPT-4.5 is based on the transformer architecture, which has established itself as the basis for most modern large language models. Openai uses the immense computing power of Microsoft Azure Ai supercomputers to train and operate GPT-4.5. The scaling of computing power and data is a decisive factor for the performance of large voice models.
One focus in the development of GPT-4.5 is on the scaling of the unsupervized learning to improve the accuracy of the world model and intuition. Openai is convinced that a deeper understanding of the world and an improved intuition are decisive for the creation of AI models that can interact with people in a natural and human way.
New scalable alignment techniques have been developed to improve cooperation with people and understanding nuances. Alignment refers to the process of aligning a AI model in such a way that it reflects the values, goals and preferences of people. Scalable alignment techniques are required to ensure that large voice models are safe, useful and ethically justifiable if they are used on a large scale.
Openaai claims that GPT-4.5 has over 10 times higher processing efficiency compared to GPT-4O. GPT-4O is an earlier model from Openai, which is also known for its conversational skills. The increase in efficiency of GPT-4.5 could make it possible to operate the model faster and cheaper and possibly also open up new areas of application.
Details on training data: scope, cutoff and the mixture of knowledge and intuition
Although the exact scope of the training data for GPT-4.5 is not publicly announced, it can be assumed that it is very large due to the skills of the model and the resources of Openaai. It is estimated that the training data petabytes or even exabytes include text and image data.
The model of the model is sufficient until September 2023. The training data probably includes a wide range of text and image data from the Internet, books, scientific publications, news articles, social media contributions and other sources. Openai probably uses sophisticated methods for data acquisition, preparation and filtering to ensure the quality and relevance of the training data.
The training of GPT-4.5 requires the use of enormous arithmetic resources and probably takes weeks or months. The exact training process is proprietary and is not described in detail by Openai. However, it can be assumed that Reinforcement Learning from Human Feedback (RLHF) plays an important role in the training process. RLHF is a technique in which human feedback is used to control the behavior of a AI model and adapt it to human preferences.
Suitable for:
Primary skills and target applications: GPT-4.5 in use
GPT-4.5 is characterized in areas such as creative writing, learning, exploring new ideas and general conversation. The model is designed to conduct natural, human and engaging conversations and to support users in a variety of tasks.
One of the most important skills of GPT-4.5 are:
Improved prompt adherence
GPT-4.5 is better to understand and implement the instructions and wishes of the users in prompts.
Context processing
The model can process longer conversations and more complex contexts and adapt its answers accordingly.
Data accuracy
GPT-4.5 has improved facts and produces fewer hallucinations than previous models.
Emotional intelligence
GPT-4.5 is able to recognize emotions in texts and to react appropriately to what leads to more natural and empathetic conversations.
Strong writing performance
GPT-4.5 can generate high-quality texts in different styles and formats, from creative texts to technical documentation.
The model has potential to optimize communication, improve content creation and support for coding and automation tasks. GPT-4.5 is particularly suitable for applications in which natural language interaction, creative generation and precise factor reproduction are in the foreground, less for complex logical reasoning.
Include some examples of target applications from GPT-4.5:
Chatbots and virtual assistants
Development of advanced chatbots and virtual assistants for customer service, education, entertainment and other areas.
Creative writing
Support of authors, screenwriters, textbers and other creatives in finding ideas, writing texts and creating creative content.
Education and learning
Use as an intelligent tutor, learning partner or research assistant in various fields of education.
Content creation
Generation of blog posts, articles, social media posts, product descriptions and other types of web content.
Translation and localization
Improvement of the quality and efficiency of machine translations and localization processes.
Availability and access for different user groups
GPT-4.5 is available for users with plus, pro, team, enterprise and edu plans. This staggered access structure enables Openai to introduce the model in a controlled manner and to address different user groups with different needs and budgets.
Developers can access GPT-4.5 via the Chat Completions API, Assistants API and Batch API. The APIs enable developers to integrate the skills of GPT-4.5 into their own applications and services.
The costs for GPT-4.5 are higher than for GPT-4O. This reflects the higher performance and additional functions of GPT-4.5, but can be an obstacle for some users.
GPT-4.5 is currently a Research Preview, and the long-term availability of the API may be limited. Openai reserves the right to change the availability and access conditions of GPT-4.5 in the future.
Microsoft also tests GPT-4.5 in Copilot Studio in a limited preview. Copilot Studio is a platform from Microsoft for the development and provision of chatbots and virtual assistants. The integration of GPT-4.5 in Copilot Studio could further expand the potential of the model for corporate applications and the automation of business processes.
Recognized strengths and weaknesses: GPT-4.5 under the magnifying glass
GPT-4.5 has received a lot of praise for his improved conversational skills and higher facts in the first user tests and ratings. The recognized strengths include:
Improved flow of conversation
GPT-4.5 leads more natural, fluid and engaging conversations than previous models.
Higher corruption
The model produces fewer hallucinations and provides more precise and reliable information.
Reduced hallucinations
Although hallucinations are still a problem with large voice models, GPT-4.5 has made significant progress in this area.
Better emotional intelligence
GPT-4.5 is better to recognize emotions in texts and to react appropriately to what leads to empathetic conversations.
Strong writing performance
The model can generate high -quality texts in different styles and formats.
Despite these strengths, there are also areas in which GPT-4.5 has its limits. The recognized weaknesses include:
Difficulties in complex reasoning
GPT-4.5 is not primarily designed for complex logical readering and can remain behind specialized models such as Deepseek in this area.
Potentially poorer performance than GPT-4O in certain logical tests
Some tests indicate that GPT-4.5 cuts less than GPT-4O in certain logical tests, which indicates that the focus may have been at the expense of conversational skills.
Higher costs than GPT-4O
GPT-4.5 is more expensive to use as a GPT-4O, which can be a factor for some users.
State of knowledge by September 2023
The limited level of knowledge of the model can be a disadvantage if current information is required.
Difficulties in self -correction and multi -stage reasoning
Some tests indicate that GPT-4.5 has difficulties in self-correction of mistakes and multi-stage logical thinking.
It is important to emphasize that GPT-4.5 is not designed to exceed models that have been developed for complex reasoning. His primary focus is on improving the conversation experience and creating AI models that can interact with people naturally.
Results of relevant benchmarks and performance comparisons: GPT-4.5 compared to its predecessors
Benchmark data show that GPT-4.5 improvements compared to GPT-4O in areas such as the right to do so and multilingual understanding, but may be lagging behind in mathematics and certain coding benchmarks.
In benchmarks such as Simpleqa (Simple Question Answering), GPT-4.5 achieves a higher accuracy and a lower hallucination rate than GPT-4O, O1 and O3-Mini. This underlines the progress that Openai has achieved when improving the correction and reduction in hallucinations.
In Reasoning benchmarks like GPQA, GPT-4.5 shows improvements compared to GPT-4O, but remains behind O3-Mini. This confirms the strengths of O3-Mini in the area of Reasoning and the tendency of GPT-4.5 to focus more on conversational skills.
In mathematics tasks (Aime), GPT-4.5 cuts significantly worse than O3-Mini. This indicates that GPT-4.5 is not as strong in mathematical reasoning as specialized models like O3-Mini.
In coding benchmarks like SWE-Lancer Diamond, GPT-4.5 shows better performance than GPT-4O. This indicates that GPT-4.5 has also made progress in codegen and analysis, although it may not be as strong as specialized coding models such as deepseek code.
Human evaluations indicate that GPT-4.5 is preferred in most cases, especially for professional inquiries. This indicates that GPT-4.5 in practice offers more convincing and useful conversation experience than its predecessors, even if it may not always achieve the best results in certain specialized benchmarks.
Suitable for:
Comparative evaluation: choosing the right AI model
The comparative analysis of the most important attributes of Gemini 2.0, Deepseek and GPT-4.5 shows significant differences and similarities between the models. Gemini 2.0 (Flash) is a transformer model with a focus on multimodality and agent functions, while Gemini 2.0 (per) uses the same architecture, but is optimized for coding and long contexts. Deepseek (R1) is based on a modified transformer with technologies such as Moe, GQA and MLA, and GPT-4.5 relies on scaling by unsupervised learning. With regard to the training data, it shows that both Gemini models and GPT-4.5 are based on large amounts of data such as text, code, images, audio and videos, while Deepseek stands out with 14.8 trillion tokens and a focus on domain-specific data as well as Reinforcement Learning (RL). The most important skills of the models vary: Gemini 2.0 offers multimodal insert and output with tool use and low latency, while the Pro version also supports a context of up to 2 million tokens. Deepseek, on the other hand, convinces with strong reasoning, coding, mathematics and multilingualism, supplemented by its open source availability. GPT-4.5 shines in particular in the areas of conversation, emotional intelligence and corruption.
The availability of the models is also different: Gemini offers APIS and a web and mobile app, while the Pro version is experimentally accessible via Vertex AI. Deepseek is available as an open source on platforms such as Hugging Face, Azure AI, Amazon DONTION and IBM Watsonx.ai. GPT-4.5, on the other hand, offers various options such as Chatgpt (Plus, Pro, Team, Enterprise, EDU) and the Openai API. The strengths of the models include multimodality and speed at Gemini 2.0 (flash) as well as the coding, the world knowledge and the long contexts at Gemini 2.0 (Pro). Deepseek scores through cost efficiency, excellent coding and math skills and strong reasoning. GPT-4.5 convinces with high factual correction and emotional intelligence. However, weaknesses can also be seen how distortions or problems with real-time problem solutions for Gemini 2.0 (flash), experimental restrictions and installment limits in the PRO version, limited multimodality and a smaller ecosystem at Deepseek as well as difficulties in complex reasoning, mathematics and limited knowledge in GPT-4.5.
The benchmark results provide further insights: Gemini 2.0 (Flash) reaches 77.6 % in MMLU, 34.5 % in Livecodebech and 90.9 % in Math, while Gemini 2.0 (per) with 79.1 % (MMLU), 36.0 % (LiveCodebech) and 91.8 % (Math) performed slightly better. Deepseek exceeds clearly with 90.8 % (MMLU), 71.5 % (GPQA), 97.3 % (Math) and 79.8 % (AIME), while GPT-4.5 sets other priorities: 71.4 % (GPQA), 36.7 % (AIME) and 62.5 % (Simpleqa).
Analysis of the most important differences and similarities
The three models Gemini 2.0, Deepseek and GPT-4.5 have both similarities and clear differences that predestine them for different areas of application and user needs.
Commonalities
Transformer architecture
All three models are based on the transformer architecture, which has established itself as a dominant architecture for large voice models.
Advanced skills
All three models demonstrate advanced skills in the processing of natural language, codegen, reasoning and other areas of AI.
MultiModality (differently pronounced):
All three models recognize the importance of multimodality, although the degree of support and focus vary.
differences
Focus and focus
- Gemini 2.0: versatility, multimodality, agent functions, wide range of applications.
- Deepseek: Efficiency, Reasoning, Coding, Mathematics, Open Source, Cost Efficiency.
- GPT-4.5: conversation, natural language interaction, correction, emotional intelligence.
Architectural innovations
Deepseek is characterized by architectural innovations such as Moe, GQA and MLA, which aim at increasing efficiency. GPT-4.5 focuses on scaling unsupervized learning and alignment techniques for improved conversational skills.
Training data
Deepseek attaches importance to domain-specific training data for coding and Chinese language, while Gemini 2.0 and GPT-4.5 are probably using more wider and more diverse data sets.
Availability and accessibility
Deepseek relies strongly on open source and offers its models via various platforms. GPT-4.5 is primarily available via Openai-owned platforms and APIs, with a staggered access model. Gemini 2.0 offers broad availability via Google services and APIs.
Strengths and weaknesses
Each model has its own strengths and weaknesses, which make it better or less suitable for certain applications.
Investigation of official publications and independent reviews: The perspective of the experts
Official publications and independent reviews essentially confirm the strengths and weaknesses of the three models shown in this report.
Official publications
Google, Deepseek Ai and Openaai regularly publish blog posts, technical reports and benchmark results in which you present your models and compare with competitors. These publications offer valuable insights into the technical details and the performance of the models, but are naturally often marketing -oriented and can have a certain bias.
Independent tests and reviews
Various independent organizations, research institutes and AI experts carry out their own tests and reviews of the models and publish their results in the form of blog posts, articles, scientific publications and benchmark comparisons. These independent reviews offer a more objective perspective on the relative strengths and weaknesses of the models and help users make an informed decision when choosing the right model for your needs.
In particular, independent reviews confirm the strengths of deepseek in mathematics and coding benchmarks and its cost efficiency compared to Openai. GPT-4.5 is praised for its improved conversation skills and the reduced hallucination rate, but its weaknesses in complex reasoning are also highlighted. Gemini 2.0 is appreciated for its versatility and multimodal skills, but its performance can vary depending on the specific benchmark.
The future of the AI is diverse
The comparative analysis of Gemini 2.0, Deepseek and GPT-4.5 clearly shows that each model has unique strengths and optimizations that make it more suitable for certain applications. There is no “the best” AI model par excellence, but rather a variety of models, each with your own advantages and limitations.
Gemini 2.0
Gemini 2.0 presents itself as a versatile family that focuses on multimodality and agent functions, with different variants that are tailored to specific needs. It is the ideal choice for applications that require comprehensive multimodal support and can benefit from the speed and versatility of the Gemini 2.0 family.
Deepseek
Deepseek is characterized by its architecture, cost efficiency and open source availability geared towards Reasoning. It is particularly strong in technical areas such as coding and mathematics and is an attractive option for developers and researchers who value performance, efficiency and transparency.
GPT-4.5
GPT-4.5 focuses on improving the user experience into conversations through increased factual corruption, reduced hallucinations and improved emotional intelligence. It is the best choice for applications that require natural and engaging conversation experience, such as: B. Chatbots, virtual assistants and creative writing.
Multimodality and open source: The trends of the upcoming AI generation
The choice of the best model depends heavily on the specific application and the priorities of the user. Companies and developers should carefully analyze their needs and requirements and weigh the strengths and weaknesses of the various models in order to make the optimal choice.
The rapid development in the field of AI models indicates that these models will continue to improve and develop quickly. Future trends could include even greater integration of multimodality, improved recurrence skills, greater accessibility through open source initiatives and broader availability on various platforms. The ongoing efforts to reduce costs and increase efficiency will continue to advance the broad acceptance and use of these technologies in various industries.
The future of the AI is not monolithic, but diverse and dynamic. Gemini 2.0, Deepseek and GPT-4.5 are just three examples of the diversity and the innovation spirit that shapes the current AI market. In the future, these models are expected to become even more powerful, more versatile and accessible and the way we interact with technology and understand the world around us. The journey of artificial intelligence has just started, and the next few years will promise even more exciting developments and breakthroughs.
We are there for you - advice - planning - implementation - project management
☑️ SME support in strategy, consulting, planning and implementation
☑️ Creation or realignment of the digital strategy and digitalization
☑️ Expansion and optimization of international sales processes
☑️ Global & Digital B2B trading platforms
☑️ Pioneer Business Development
Konrad Wolfenstein
I would be happy to serve as your personal advisor.
You can contact me by filling out the contact form below or simply call me on +49 89 89 674 804 (Munich) .
I'm looking forward to our joint project.
Write to me
Xpert.Digital - Konrad Wolfenstein
Xpert.Digital is a hub for industry with a focus on digitalization, mechanical engineering, logistics/intralogistics and photovoltaics.
With our 360° business development solution, we support well-known companies from new business to after sales.
Market intelligence, smarketing, marketing automation, content development, PR, mail campaigns, personalized social media and lead nurturing are part of our digital tools.
You can find out more at: www.xpert.digital - www.xpert.solar - www.xpert.plus
Keep in touch
