Comparative analysis of leading AI models: Google Gemini 2.0, DeepSeek R2 and GPT-4.5 from OpenAI

The Rise of Intelligent Machines

We are living in an era of unprecedented progress in the field of artificial intelligence (AI). The development of large language models (LLMs) has reached a speed in recent years that has surprised many experts and observers. These sophisticated AI systems are no longer just tools for specialized applications; they are permeating ever more areas of our lives, changing the way we work, communicate, and understand the world around us.

At the forefront of this technological revolution are three models that are causing a stir in the scientific community and beyond: Gemini 2.0 from Google DeepMind, DeepSeek from DeepSeek AI, and GPT-4.5 from OpenAI. These models represent the current state of the art in AI research and development. They demonstrate impressive capabilities across a wide range of disciplines, from natural language processing and computer code generation to complex logical reasoning and creative content creation.

This report undertakes a comprehensive and comparative analysis of these three models to examine their respective strengths, weaknesses, and areas of application in detail. The aim is to create a deep understanding of the differences and similarities between these cutting-edge AI systems and to provide an informed basis for assessing their potential and limitations. In doing so, we will not only investigate the technical specifications and performance data, but also the underlying philosophical and strategic approaches of the developers who shaped these models.

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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 dominated by a few, but very influential, players. Google DeepMind, DeepSeek AI, and OpenAI are not just technology companies; they are also research institutions at the forefront of AI innovation. Their models are not just products, but also manifestations of their respective visions of the future of AI and its role in society.

Google DeepMind, with its deep roots in research and immense computing power, is pursuing a versatile and multimodal approach with Gemini 2.0. The company envisions the future of AI in intelligent agents capable of handling complex real-world tasks while seamlessly processing and generating various types of information – text, images, audio, and video.

DeepSeek AI, an emerging company based in China, has made a name for itself with DeepSeek, which stands out for its remarkable efficiency, strong reasoning capabilities, and commitment to open source. DeepSeek positions itself as a challenger in the AI ​​market, offering a powerful yet accessible alternative to the models of established giants.

OpenAI, known for ChatGPT and the GPT model family, has once 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 capable of interacting with humans on a deeper level. GPT-4.5 embodies this vision and aims to push the boundaries 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 needs of the modern AI ecosystem. This family includes various variants, each tailored to specific application areas and performance requirements.

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Recent developments and announcements (as of March 2025): The Gemini family is growing

Throughout 2025, Google DeepMind continuously introduced new members of the Gemini 2.0 family, underscoring its ambitions in the AI ​​market. Of particular note is the general availability of Gemini 2.0 Flash and Gemini 2.0 Flash-Lite, which are positioned as powerful and cost-effective options for developers.

Gemini 2.0 Flash is described by Google itself as a "workhorse" model. This designation highlights its strengths in terms of speed, reliability, and versatility. It is designed to deliver high performance with low latency, making it ideal for applications where fast response times are critical, such as chatbots, real-time translations, or interactive applications.

Gemini 2.0 Flash-Lite, on the other hand, aims for maximum cost efficiency. This model is optimized for high-throughput applications where low operating costs per request are crucial, such as bulk text processing, automated content moderation, or the delivery of AI services in resource-constrained 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 under development and serve to explore the limits of what is possible in AI research and to gather early feedback from developers and researchers.

Gemini 2.0 Pro is highlighted as the most powerful model in the family, particularly in coding and world knowledge. A notable feature is its extremely long context window of 2 million tokens. This means that Gemini 2.0 Pro is capable of processing and understanding extremely large amounts of text, making it ideal for tasks requiring a deep understanding of complex relationships, such as analyzing extensive documentation, answering complex questions, or generating code for large software projects.

Gemini 2.0 Flash Thinking Experimental, on the other hand, focuses on improving reasoning capabilities. This model is able to explicitly represent its thought process to enhance performance and increase the explainability of AI decisions. This feature is particularly important in application areas where transparency and traceability of AI decisions are crucial, such as medicine, finance, and law.

Another important aspect of the recent developments with Gemini 2.0 is Google's discontinuation of older models in the Gemini 1.x series, as well as the PaLM and Codey models. The company strongly recommends that users of these older models migrate to Gemini 2.0 Flash to avoid service interruptions. This move suggests that Google is confident in the advancements in the architecture and performance of the Gemini 2.0 generation and intends to position it as the future platform for its AI services.

The global reach of Gemini 2.0 Flash is underscored by its availability via the Gemini web application in more than 40 languages ​​and over 230 countries and territories. This demonstrates Google's commitment to democratizing access to advanced AI technology and its vision of AI that is accessible and usable for people worldwide.

Architectural overview and technological foundations: Focus on multimodality and agent functions

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 capable of interacting with the real world, using tools, generating images, and understanding and producing speech. These multimodal capabilities and agent functions are the result of a profound architectural focus on the needs of future AI applications.

The various versions of Gemini 2.0 are each focused on different areas to cover a wide range of use cases. Gemini 2.0 Flash is designed as a versatile, low-latency model suitable for a broad spectrum of tasks. Gemini 2.0 Pro, on the other hand, specializes in coding, world knowledge, and long contexts, targeting users who require top performance in these areas. Gemini 2.0 Flash-Lite is intended for cost-optimized applications, offering a balance between performance and economics. Finally, Gemini 2.0 Flash Thinking Experimental aims to enhance reasoning capabilities and explores new ways to improve the logical thinking processes of AI models.

A key feature of the Gemini 2.0 architecture is its support for multimodal input. The models can process text, code, images, audio, and video as input, thus integrating information from various sensory modalities. Output can also be multimodal, with Gemini 2.0 capable of generating text, images, and audio. Some output modalities, such as video, are currently in private preview and are expected to be generally available in the future.

The impressive performance of Gemini 2.0 is also due to Google's investment in specialized hardware. The company relies on its own Trillium TPUs (Tensor Processing Units), which were specifically designed to accelerate AI calculations. This custom-built hardware allows Google to train and run its AI models more efficiently, thus gaining a competitive advantage in the AI ​​market.

Gemini 2.0's architectural focus on multimodality and enabling AI agents to interact with the real world is a key differentiator from other AI models. The existence of different variants within the Gemini 2.0 family suggests a modular approach, allowing Google to flexibly adapt the models to specific performance or cost requirements. The use of its own hardware underscores Google's long-term commitment to advancing 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 composition of the training data for Gemini 2.0 is not publicly available, the model's capabilities suggest that it was trained on massive datasets. These datasets likely comprise terabytes or even petabytes of text and code data, as well as multimodal data for the 2.0 versions, including images, audio, and video.

Google possesses an invaluable treasure trove of data drawn from across the internet, including digitized books, scientific publications, news articles, social media posts, and countless other sources. This vast amount of data forms the basis for training Google's AI models. It can be assumed that Google employs sophisticated methods to ensure the quality and relevance of the training data and to filter out potential biases or unwanted content.

Gemini 2.0's multimodal capabilities require the inclusion of image, audio, and video data in the training process. This data likely originates from various sources, including publicly available image databases, audio archives, video platforms, and possibly proprietary datasets from Google. The challenge of multimodal data collection and processing lies in meaningfully integrating the different data modalities and ensuring that the model learns the connections and relationships between them.

The training process for large language models like Gemini 2.0 is extremely computationally intensive and requires the use of powerful supercomputers and specialized AI hardware. It is an iterative process in which the model is repeatedly fed training data and its parameters are adjusted until it performs the desired tasks. This process can take weeks or even months and requires a deep understanding of the underlying algorithms and the intricacies of machine learning.

Key capabilities and diverse applications: Gemini 2.0 in action

Gemini 2.0 Flash, Pro, and Flash-Lite offer an impressive range of capabilities, making them suitable for a wide variety of applications across different industries and sectors. Key features include:

Multimodal input and output

The ability to process and generate text, code, images, audio and video opens up new possibilities for human-machine interaction and the creation of multimodal content.

Tool usage

Gemini 2.0 can leverage external tools and APIs to access information, execute actions, and handle complex tasks. This allows the model to go beyond its own capabilities and adapt to dynamic environments.

Long context windows

In particular, Gemini 2.0 Pro, with its 2-million-token context window, can process and understand extremely long texts, making it ideal for tasks such as analyzing extensive documents or summarizing long conversations.

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 excels in coding and can generate high-quality code in various programming languages, detect and fix errors in the code, and assist in software development.

Function Calling

The ability to call functions allows Gemini 2.0 to interact with other systems and applications and to automate complex workflows.

The potential applications of Gemini 2.0 are virtually limitless. Some examples include:

Content creation

Generation of texts, articles, blog posts, screenplays, 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

Supporting software developers with code generation, bug fixing, code documentation, and learning new programming languages.

Improved search experiences

Smarter and more contextual search results that go beyond traditional keyword searches, helping users answer complex questions and gain deeper insights into information.

Business and enterprise applications

Deployment 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 like Project Astra, which explores the future capabilities of a universal AI assistant, and Project Mariner, a browser automation prototype, demonstrate the practical applications of Gemini 2.0. These projects show that Google sees Gemini technology not only as a tool for individual tasks, but as the foundation for developing comprehensive AI solutions capable of supporting people in their daily lives and professional activities.

The versatility of the Gemini 2.0 model family allows it to be used in a wide range of tasks, from general applications to specialized areas such as coding and complex reasoning. The focus on agent functions indicates a trend toward more proactive and helpful AI systems that not only respond to commands but are also capable of acting independently and solving problems.

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Availability and accessibility for users and developers: AI for all

Google is actively working to make Gemini 2.0 accessible to both developers and end users. Gemini 2.0 Flash and Flash-Lite are available through the Gemini API in Google AI Studio and Vertex AI. Google AI Studio is a web-based development environment that allows developers to experiment with Gemini 2.0, create prototypes, and build AI applications. Vertex AI is Google's cloud platform for machine learning, offering a comprehensive suite of tools and services for training, deploying, and managing AI models.

The experimental version Gemini 2.0 Pro is also accessible in Vertex AI, but is aimed more at advanced users and researchers who want to explore the latest features and capabilities of the model.

A chat-optimized version of Gemini 2.0 Flash Experimental is available in the Gemini web application and mobile app. This allows end users to experience the capabilities of Gemini 2.0 in a conversational context and provide feedback that contributes to the further development of the model.

Furthermore, Gemini is integrated into Google Workspace applications such as Gmail, Docs, Sheets, and Slides. This integration allows users to leverage Gemini 2.0's AI capabilities directly in their daily workflows, for example, when composing emails, creating documents, analyzing data in spreadsheets, or creating presentations.

The phased release of Gemini 2.0, from experimental versions to generally available models, allows for a controlled rollout and the collection of user feedback. This is a key aspect of Google's strategy to ensure that the models are stable, reliable, and user-friendly before being made available to a wider audience. Integration with widely used platforms like Google Workspace makes it easier for a broad user base to leverage the model's capabilities and helps integrate AI into people's everyday lives.

Known strengths and weaknesses: An honest look at Gemini 2.0

Gemini 2.0 has received much praise in the AI ​​community and in initial user tests for its impressive capabilities. Reported strengths include:

Improved multimodal capabilities

Gemini 2.0 surpasses its predecessors and many other models in the processing and generation of multimodal data, making it ideal for a wide range of applications in the media, communications and creative industries.

Faster processing

Gemini 2.0 Flash and Flash-Lite are optimized for speed and offer low latency, making them ideal for real-time applications and interactive systems.

Improved reasoning and contextual understanding

Gemini 2.0 demonstrates progress in logical reasoning and understanding complex contexts, leading to more accurate and relevant answers and results.

Strong performance in encoding and processing long contexts

In particular, Gemini 2.0 Pro impresses with its capabilities in code generation and analysis, as well as with its extremely long context window, which allows it to process large amounts of text.

Despite these impressive strengths, there are also areas where Gemini 2.0 still has room for improvement. Reported weaknesses include:

Potential distortions

Like many large language models, Gemini 2.0 can reflect biases in its training data, which can lead to prejudiced or discriminatory results. Google is actively working to identify and minimize these biases.

Limitations in complex real-time problem solving

Although Gemini 2.0 shows progress in reasoning, it can still reach its limits with very complex problems in real time, especially compared to specialized models optimized for certain types of reasoning tasks.

Needs improvement in the 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 room for improvement, e.g. in terms of stylistic consistency or the consideration of specific user preferences.

Compared to competitors like Grok and GPT-4, Gemini 2.0 shows strengths in multimodal tasks, but may lag behind in certain reasoning benchmarks. It's important to emphasize that the AI ​​market is very dynamic and the relative performance of different models is constantly changing.

Overall, Gemini 2.0 offers impressive capabilities and represents a significant advancement in the development of large language models. However, like other LLMs, it also faces challenges regarding bias and consistent reasoning across all tasks. Google DeepMind's continuous development and improvement of Gemini 2.0 is expected to further minimize these weaknesses and enhance its strengths in the future.

Results of relevant benchmarks and performance comparisons: Numbers speak volumes

Benchmark data shows that Gemini 2.0 Flash and Pro exhibit a significant performance increase compared to their predecessors in various established benchmarks such as MMLU (Massive Multitask Language Understanding), LiveCodeBench, Bird-SQL, GPQA (Graduate-Level Google-Proof Q&A), MATH, HiddenMath, Global MMLU, MMMU (Massive Multi-discipline Multimodal Understanding), COGoST2 (Conversational Voice to Speech Translation) and EgoSchema.

The different versions of Gemini 2.0 exhibit different strengths, with Pro generally performing better in more complex tasks, while Flash and Flash-Lite are optimized for speed and cost efficiency.

Compared to models from other companies like GPT-4o and DeepSeek, the relative performance varies depending on the specific benchmark and the models being compared. For example, Gemini 2.0 outperforms Flash 1.5 Pro in key benchmarks while being twice as fast. This highlights the efficiency gains Google has achieved through the further development of the Gemini architecture.

Gemini 2.0 Pro achieves higher scores than Gemini 1.5 Pro in areas such as SWE-bench Accuracy (Software Engineering Benchmark), Code Debugging Speed, and Multi-file Consistency. These improvements are particularly relevant for software developers and companies that use AI for code generation and analysis.

In mathematics benchmarks like MATH and HiddenMath, the 2.0 models also show significant improvements over their predecessors. This suggests that Google has made progress in improving the reasoning capabilities of Gemini 2.0, particularly in areas requiring logical thinking and mathematical understanding.

However, it's important to note that benchmark results only represent part of the overall picture. The actual performance of an AI model in real-world applications can vary depending on the specific requirements and context. Nevertheless, benchmark data provides valuable insights into the relative strengths and weaknesses of different models and allows for an objective comparison of their performance.

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DeepSeek: The efficient challenger with a focus on reasoning and open source

DeepSeek is an AI model developed by DeepSeek AI, distinguished by its remarkable efficiency, strong reasoning capabilities, and commitment to open source. Positioning itself as a powerful and cost-effective alternative to the models of established AI giants, DeepSeek has already garnered significant attention within the AI ​​community.

Architectural framework and technical specifications: Efficiency through innovation

DeepSeek uses a modified Transformer architecture that prioritizes efficiency through Grouped Query Attention (GQA) and dynamic Sparse Activation (Mixture of Experts – MoE). These architectural innovations enable DeepSeek to achieve high performance with comparatively low computational resources.

The DeepSeek R1 model, the first publicly available version of DeepSeek, has 671 billion parameters, but only 37 billion are activated per token. This "sparse activation" approach significantly reduces computational costs during inference, as only a small portion 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.

DeepSeek focuses on balancing performance with practical limitations, particularly in code generation and multilingual support. The model is designed to deliver excellent results in these areas while remaining cost-effective and resource-efficient.

The MoE architecture used by DeepSeek divides the AI ​​model into separate subnetworks, each specializing in a subset of the input data. During training and inference, only a subset of the subnetworks is activated for each input, significantly reducing computational costs. This approach allows DeepSeek to train and run a very large model with many parameters without excessively increasing inference speed or cost.

Insights into training data: Quality over quantity and the value of specialization

DeepSeek places great emphasis on domain-specific training data, particularly for coding and the Chinese language. The company believes that the quality and relevance of the training data are more crucial to the performance of an AI model than sheer quantity.

DeepSeek-V3's training corpus comprises 14.8 trillion tokens. A significant portion of this data originates from domain-specific sources focused on coding and the Chinese language. This enables DeepSeek to perform exceptionally well in these areas.

DeepSeek's training methodology incorporates 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 machine learning method in which an agent learns to behave in an environment by receiving rewards for desired actions and punishments for undesired actions.

DeepSeek-R1-Zero was trained without initial supervised fine-tuning (SFT) to promote reasoning skills purely through reinforcement learning. Supervised fine-tuning is a common technique where a pre-trained language model is fine-tuned with a smaller, annotated dataset to improve its performance on specific tasks. However, DeepSeek has shown that it is possible to achieve strong reasoning skills without SFT, using reinforcement learning alone.

DeepSeek-R1, on the other hand, integrates cold-start data before reinforcement learning to create a strong foundation for both reasoning and non-reasoning tasks. Cold-start data is data used at the beginning of training to give the model a basic understanding of language and the world. By combining cold-start data with reinforcement learning, DeepSeek can train a model that possesses both strong reasoning skills and broad general knowledge.

Advanced techniques such as Group Relative Policy Optimization (GRPO) are also used to optimize the RL training process and improve the stability and efficiency of the training.

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Core capabilities and potential use cases: DeepSeek in action

DeepSeek-R1 is characterized by a number of core capabilities that make it ideal for various use cases:

Strong reasoning skills

DeepSeek-R1 excels in logical reasoning and problem-solving, particularly in areas such as mathematics and coding.

Superior performance in coding and mathematics

Benchmark data shows that DeepSeek-R1 often performs better than many other models in coding and mathematics benchmarks, including some models from OpenAI.

Multilingual support

DeepSeek-R1 offers support for multiple languages, making it attractive for global applications and multilingual users.

Cost efficiency

DeepSeek-R1's efficient architecture allows the model to be operated with comparatively low computing costs, making it a cost-effective option for businesses and developers.

Open Source Availability

DeepSeek AI is committed to the open-source philosophy and makes many of its models, including DeepSeek LLM and DeepSeek Coder, available as open source. This promotes transparency, collaboration, and the further development of AI technology by the community.

Potential use cases 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

Deployment as an intelligent tutor in the fields of mathematics, computer science and other technical disciplines to support learners in problem-solving and understanding complex concepts.

Development tools

Integration into development environments and tools to support software developers in code generation, debugging, 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 code generation for visualizations. This demonstrates DeepSeek's potential to create added value even in specialized and complex application areas.

DeepSeek-R1 can solve complex problems by breaking them down into individual steps and making the thought process transparent. This capability is particularly valuable in application areas where traceability and explainability of AI decisions are important.

Availability and licensing options: Open source for innovation and accessibility

DeepSeek strongly embraces open source and has released several of its models under open-source licenses. DeepSeek LLM and DeepSeek Coder are available as open source and can be freely used, modified, and further developed by the community.

DeepSeek-R1 is released under the MIT license, a very liberal open-source license that permits commercial and non-commercial use, modification, and redistribution of the model. This open-source strategy distinguishes DeepSeek from many other AI companies that typically keep their models proprietary.

DeepSeek-R1 is available on various platforms, including Hugging Face, Azure AI Foundry, Amazon Bedrock, and IBM watsonx.ai. Hugging Face is a popular platform for publishing and sharing AI models and datasets. Azure AI Foundry, Amazon Bedrock, and IBM watsonx.ai are cloud platforms that provide access to DeepSeek-R1 and other AI models via APIs.

DeepSeek's models are known for being cost-effective compared to competitors, both in terms of training and inference costs. This is a significant advantage for companies and developers who want to integrate AI technology into their products and services but need to be mindful of their budgets.

DeepSeek's commitment to open source and cost-efficiency makes it an attractive option for a wide range of users, from researchers and developers to businesses and organizations. Open-source availability fosters transparency, collaboration, and faster development of DeepSeek technology by the AI ​​community.

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Reported strengths and weaknesses: A critical look at DeepSeek

DeepSeek has received much recognition in the AI ​​community for its strengths in coding, mathematics, and reasoning. 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 that of OpenAI models.

Cost efficiency

DeepSeek-R1's efficient architecture allows the model to be run at lower computational 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 skills

DeepSeek-R1 demonstrates impressive capabilities in logical reasoning and problem-solving, particularly in technical domains.

Despite these strengths, there are also areas where DeepSeek still has room for improvement. Reported weaknesses include:

Potential distortions

Like all large language models, DeepSeek may reflect biases in its training data, although DeepSeek AI strives to minimize these.

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 as established providers such as Google or OpenAI.

Limited multimodal support beyond text and code

DeepSeek focuses primarily on text and code processing and currently does not offer comprehensive multimodal support for images, audio and video like Gemini 2.0.

Still requires human supervision

Although DeepSeek-R1 delivers impressive performance in many areas, human oversight and validation are still required in critical use cases to avoid errors or unwanted results.

Occasional hallucinations

Like all large language models, DeepSeek can occasionally produce hallucinations, i.e., generate false or irrelevant information.

dependence on large computing resources

The training and operation of DeepSeek-R1 require significant computing resources, although the efficient architecture of the model reduces these requirements compared to other models.

Overall, DeepSeek is a promising AI model with particular strengths in coding, mathematics, and reasoning. Its cost-effectiveness and open-source availability make it an attractive option for many users. The further development of DeepSeek AI is expected to minimize its weaknesses and enhance its strengths in the future.

Results of relevant benchmarks and performance comparisons: DeepSeek in comparison

Benchmark data shows that DeepSeek-R1 can keep pace with or even outperform OpenAI-o1 in many reasoning benchmarks, particularly in mathematics and coding. OpenAI-o1 here refers to earlier OpenAI models released before GPT-4.5, which may still be competitive in certain areas, such as reasoning.

In mathematics benchmarks such as AIME 2024 (American Invitational Mathematics Examination) and MATH-500, DeepSeek-R1 achieves high scores and often outperforms OpenAI models. This underscores DeepSeek's strengths in mathematical reasoning and problem-solving.

In the area of ​​coding, DeepSeek-R1 also demonstrates strong performance in benchmarks such as LiveCodeBench and Codeforces. LiveCodeBench is a code generation benchmark, while Codeforces is a platform for programming competitions. DeepSeek-R1's good results in these benchmarks indicate its ability to generate high-quality code and solve complex programming tasks.

In general knowledge benchmarks like GPQA Diamond (Graduate-Level Google-Proof Q&A), DeepSeek-R1 often performs on par with or slightly below OpenAI-o1. GPQA Diamond is a demanding benchmark that tests the general knowledge and reasoning abilities of AI models. The results suggest that DeepSeek-R1 is also competitive in this area, although it may not quite reach the same level of performance as specialized models.

The distilled versions of DeepSeek-R1, based on smaller models like Llama and Qwen, also show impressive results in various benchmarks, in some cases even surpassing OpenAI-o1-mini. Distillation is a technique where a smaller model is trained to mimic the behavior of a larger model. The distilled versions of DeepSeek-R1 demonstrate that DeepSeek's core technology can be effectively used in smaller models, highlighting its versatility and scalability.

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GPT-4.5: Conversational Excellence and the Focus on Natural Interaction

GPT-4.5, codenamed “Orion,” is OpenAI’s latest flagship model and embodies the company’s vision of an AI that is not only intelligent but also intuitive, empathetic, and capable of interacting with humans on a deep level. GPT-4.5 focuses primarily on improving the conversational experience, increasing factual accuracy, and reducing hallucinations.

Current specifications and key features (as of March 2025): GPT-4.5 revealed

GPT-4.5 was released as a Research Preview in February 2025 and is described by OpenAI itself as the “biggest and best chat model” to date. This statement underscores the model's primary focus on conversational capabilities and optimizing human-machine interaction.

The model has a context window of 128,000 tokens and a maximum output length of 16,384 tokens. While the context window is smaller than that of Gemini 2.0 Pro, it is still very large and allows GPT-4.5 to conduct longer conversations and handle more complex queries. The maximum output length limits the length of the responses the model can generate.

The knowledge base of GPT-4.5 extends to September 2023. This means that the model has information and events up to that point, but no knowledge of subsequent developments. This is an important limitation that must be considered when using GPT-4.5 for time-critical or current information.

GPT-4.5 integrates features such as web search, file and image uploads, and the Canvas tool into ChatGPT. Web search allows the model to access current information from the internet and enrich its responses with up-to-date knowledge. File and image uploads allow users to provide the model with additional information in the form of files or images. The Canvas tool is an interactive drawing board that allows users to incorporate visual elements into their conversations with GPT-4.5.

Unlike models such as o1 and o3-mini, which focus on stepwise reasoning, GPT-4.5 scales up unsupervised learning. Unsupervised learning is a machine learning method where the model learns from unannotated data without explicit instructions or labels. This approach aims to make the model more intuitive and conversational, but may potentially come at the expense of performance on complex problem-solving tasks.

Architectural Design and Innovations: Scaling and Alignment for Conversation

GPT-4.5 is based on the Transformer architecture, which has become the foundation for most modern large language models. OpenAI leverages the immense computing power of Microsoft Azure AI supercomputers to train and run GPT-4.5. Scaling computing power and data is a crucial factor in the performance of large language models.

A key focus in the development of GPT-4.5 is scaling unsupervised learning to improve the accuracy of the world model and intuition. OpenAI believes that a deeper understanding of the world and improved intuition are crucial for creating AI models that can interact with people in a natural and human-like way.

New scalable alignment techniques have been developed to improve collaboration with humans and the understanding of nuances. Alignment refers to the process of aligning an AI model to reflect human values, goals, and preferences. Scalable alignment techniques are necessary to ensure that large language models are safe, useful, and ethically sound when deployed at scale.

OpenAI claims that GPT-4.5 offers over 10 times the processing efficiency of GPT-4o, an earlier OpenAI model also known for its conversational capabilities. The increased efficiency of GPT-4.5 could allow the model to run faster and more cost-effectively, potentially opening up new application areas.

Details on training data: scope, cutoff, and the mix of knowledge and intuition

Although the exact size of the training data for GPT-4.5 is not publicly disclosed, it is assumed to be very large due to the model's capabilities and OpenAI's resources. It is estimated that the training data comprises petabytes or even exabytes of text and image data.

The model's knowledge base extends to September 2023. The training data likely comprises diverse text and image data from the internet, books, scientific publications, news articles, social media posts, and other sources. OpenAI probably uses sophisticated methods for data collection, preparation, and filtering to ensure the quality and relevance of the training data.

Training GPT-4.5 requires enormous computing resources and likely takes weeks or months. The exact training process is proprietary and not publicly described in detail by OpenAI. However, it can be assumed that Reinforcement Learning from Human Feedback (RLHF) plays a significant role in the training process. RLHF is a technique that uses human feedback to guide the behavior of an AI model and adapt it to human preferences.

Suitable for:

• Agentic Ai | Latest developments in Chatgpt from Openai: Deep Research, GPT-4.5 / GPT-5, emotional intelligence and precision

Primary capabilities and target applications: GPT-4.5 in use

GPT-4.5 excels in areas such as creative writing, learning, exploring new ideas, and general conversation. The model is designed to facilitate natural, human, and engaging conversations and to support users in a wide range of tasks.

The most important capabilities of GPT-4.5 include:

Improved prompt adherence

GPT-4.5 is better at understanding and implementing user instructions and requests in prompts.

Context processing

The model can process longer conversations and more complex contexts and adjust its responses accordingly.

Data accuracy

GPT-4.5 exhibits improved factual accuracy and produces fewer hallucinations than previous models.

Emotional intelligence

GPT-4.5 is able to recognize emotions in texts and respond appropriately, leading to more natural and empathetic conversations.

Strong writing performance

GPT-4.5 can generate high-quality texts in various styles and formats, from creative texts to technical documentation.

The model has the potential to optimize communication, improve content creation, and support coding and automation tasks. GPT-4.5 is particularly well-suited for applications that prioritize natural language interaction, creative generation, and accurate factual representation, rather than complex logical reasoning.

Some examples of target applications for GPT-4.5 include:

Chatbots and virtual assistants

Development of advanced chatbots and virtual assistants for customer service, education, entertainment and other areas.

Creative Writing

Support for authors, screenwriters, copywriters and other creatives in brainstorming, writing texts and creating creative content.

Education and learning

Deployment as an intelligent tutor, learning partner or research assistant in various educational fields.

Content creation

Generation of blog posts, articles, social media posts, product descriptions and other types of web content.

Translation and localization

Improving the quality and efficiency of machine translations and localization processes.

Availability and access for different user groups

GPT-4.5 is available to users with Plus, Pro, Team, Enterprise, and Edu plans. This tiered access structure allows OpenAI to roll out the model in a controlled manner and address different user groups with varying needs and budgets.

Developers can access GPT-4.5 via the Chat Completions API, Assistants API, and Batch API. These APIs allow developers to integrate the capabilities of GPT-4.5 into their own applications and services.

The cost of GPT-4.5 is higher than that of GPT-40. This reflects the higher performance and additional features of GPT-4.5, but may be a barrier 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 is also testing GPT-4.5 in a limited preview within Copilot Studio. Copilot Studio is a Microsoft platform for developing and deploying chatbots and virtual assistants. Integrating GPT-4.5 into Copilot Studio could further expand the model's potential for enterprise applications and business process automation.

Recognized strengths and weaknesses: GPT-4.5 under scrutiny

GPT-4.5 has received much praise in initial user tests and reviews for its improved conversational skills and higher factual accuracy. Among its recognized strengths are:

Improved conversation flow

GPT-4.5 leads to more natural, fluid, and engaging conversations than previous models.

Higher factual accuracy

The model produces fewer hallucinations and delivers more accurate and reliable information.

Reduced hallucinations

Although hallucinations are still a problem in large language models, GPT-4.5 has made significant progress in this area.

Improved emotional intelligence

GPT-4.5 is better at recognizing emotions in texts and responding appropriately, leading to more empathetic conversations.

Strong writing performance

The model can generate high-quality texts in various styles and formats.

Despite these strengths, there are also areas where GPT-4.5 has its limitations. Recognized weaknesses include:

Difficulties with complex reasoning

GPT-4.5 is not primarily designed for complex logical reasoning and may lag behind specialized models like DeepSeek in this area.

Potentially worse performance than GPT-4o in certain logic tests

Some tests indicate that GPT-4.5 performs worse than GPT-40 in certain logic tests, suggesting that the focus on conversational skills may have come at the expense of reasoning performance.

Higher costs than GPT-40

GPT-4.5 is more expensive to use than GPT-40, which may be a factor for some users.

State of knowledge as of September 2023

The model's limited knowledge base can be a disadvantage when up-to-date information is needed.

Difficulties with self-correction and multi-stage reasoning

Some tests suggest that GPT-4.5 has difficulties with self-correction of errors and multi-stage logical reasoning.

It is important to emphasize that GPT-4.5 is not designed to outperform models developed for complex reasoning. Its primary focus is on improving the conversational experience and creating AI models that can interact with humans in a natural and human-like way.

Results of relevant benchmarks and performance comparisons: GPT-4.5 compared to its predecessors

Benchmark data show that GPT-4.5 has improvements over GPT-4o in areas such as factual accuracy and multilingual comprehension, but may lag behind in mathematics and certain coding benchmarks.

In benchmarks such as SimpleQA (Simple Question Answering), GPT-4.5 achieves higher accuracy and a lower hallucination rate than GPT-4o, o1, and o3-mini. This underscores the progress OpenAI has made in improving factual accuracy and reducing hallucinations.

In reasoning benchmarks like GPQA, GPT-4.5 shows improvements over GPT-40, but lags behind o3-mini. This confirms the strengths of o3-mini in reasoning and the tendency of GPT-4.5 to focus more on conversational skills.

In mathematics tasks (AIME), GPT-4.5 performs significantly worse than o3-mini. This suggests 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-40. This suggests that GPT-4.5 has also made progress in code generation and analysis, although it may not be as powerful as specialized coding models like DeepSeek Coder.

Human evaluations indicate that GPT-4.5 is preferred in most cases, especially for professional inquiries. This suggests that, in practice, GPT-4.5 offers a more compelling and useful conversational experience than its predecessors, even if it may not always achieve the best results in certain specialized benchmarks.

Suitable for:

• Current developments in Chatgpt von Openaai (March 2025)

Comparative assessment: Choosing the right AI model

A comparative analysis of the key attributes of Gemini 2.0, DeepSeek, and GPT-4.5 reveals 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 (Pro) 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 through unsupervised learning. Regarding training data, both Gemini models and GPT-4.5 are based on large datasets such as text, code, images, audio, and video, while DeepSeek stands out with 14.8 trillion tokens and a focus on domain-specific data and reinforcement learning (RL). The models' key capabilities vary: Gemini 2.0 offers multimodal input and output with tool usage and low latency, while the Pro version additionally supports a context of up to 2 million tokens. DeepSeek, on the other hand, impresses with strong reasoning, coding, mathematics, and multilingual capabilities, complemented by its open-source availability. GPT-4.5 particularly excels in the areas of conversation, emotional intelligence, and factual accuracy.

The availability of the models also varies: Gemini offers APIs as well as a web and mobile app, while the Pro version is available experimentally via Vertex AI. DeepSeek is available as open source on platforms such as HuggingFace, Azure AI, Amazon Bedrock, 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 models' strengths include multimodality and speed in Gemini 2.0 (Flash), and coding, world knowledge, and long contexts in Gemini 2.0 (Pro). DeepSeek scores points for cost-efficiency, excellent coding and mathematical capabilities, and strong reasoning. GPT-4.5 impresses with high factual accuracy and emotional intelligence. However, weaknesses can also be identified, such as distortions or problems with real-time problem solving in Gemini 2.0 (Flash), experimental limitations and rate restrictions in the Pro version, limited multimodality and a smaller ecosystem in DeepSeek, as well as difficulties with complex reasoning, mathematics and the limited knowledge in GPT-4.5.

The benchmark results provide further insights: Gemini 2.0 (Flash) achieves 77.6% in MMLU, 34.5% in LiveCodeBench, and 90.9% in MATH, while Gemini 2.0 (Pro) performs slightly better with 79.1% (MMLU), 36.0% (LiveCodeBench), and 91.8% (MATH). DeepSeek significantly outperforms these benchmarks with 90.8% (MMLU), 71.5% (GPQA), 97.3% (MATH), and 79.8% (AIME), while GPT-4.5 focuses on different areas: 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 significant differences that make them suitable for different applications and user needs.

Commonalities

Transformer architecture

All three models are based on the Transformer architecture, which has established itself as the dominant architecture for large language models.

Advanced skills

All three models demonstrate advanced capabilities in natural language processing, code generation, reasoning, and other areas of AI.

Multimodality (to varying degrees):

All three models recognize the importance of multimodality, although the level of support and focus vary.

differences

Focus and key areas

• Gemini 2.0: Versatility, multimodality, agent functions, broad range of applications.
• DeepSeek: Efficiency, Reasoning, Coding, Mathematics, Open Source, Cost Efficiency.
• GPT-4.5: Conversation, natural language interaction, factual accuracy, emotional intelligence.

Architectural innovations

DeepSeek features architectural innovations such as MoE, GQA, and MLA, which aim to increase efficiency. GPT-4.5 focuses on scaling unsupervised learning and alignment techniques for improved conversational skills.

Training data

DeepSeek emphasizes domain-specific training data for coding and Chinese language, while Gemini 2.0 and GPT-4.5 are likely to use broader and more diverse datasets.

Availability and accessibility

DeepSeek relies heavily on open source and offers its models across various platforms. GPT-4.5 is primarily available through OpenAI's own platforms and APIs, with a tiered access model. Gemini 2.0 offers broad availability through Google services and APIs.

Strengths and weaknesses

Each model has its own strengths and weaknesses that make it more or less suitable for certain applications.

Examination of official publications and independent assessments: The experts' perspective

Official publications and independent assessments essentially confirm the strengths and weaknesses of the three models presented in this report.

Official publications

Google, DeepSeek AI, and OpenAI regularly publish blog posts, technical reports, and benchmark results showcasing their models and comparing them to competitors. These publications offer valuable insights into the technical details and performance of the models, but are inherently often marketing-driven and may exhibit some bias.

Independent tests and reviews

Various independent organizations, research institutes, and AI experts conduct their own tests and evaluations of the models and publish their results in the form of blog posts, articles, scientific publications, and benchmark comparisons. These independent assessments offer a more objective perspective on the relative strengths and weaknesses of the models and help users make an informed decision when selecting the right model for their needs.

Independent reviews, in particular, confirm DeepSeek's strengths in mathematics and coding benchmarks and its cost-effectiveness compared to OpenAI. GPT-4.5 is praised for its improved conversational capabilities and reduced hallucination rate, but its weaknesses in complex reasoning are also highlighted. Gemini 2.0 is valued for its versatility and multimodal capabilities, but its performance can vary depending on the specific benchmark.

The future of AI is multifaceted

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 better suited for specific use cases. There is no single "best" AI model, but rather a variety of models, each with its own advantages and limitations.

Gemini 2.0

Gemini 2.0 presents itself as a versatile family that prioritizes multimodality and agent functionality, with various variants tailored to specific needs. It is the ideal choice for applications requiring comprehensive multimodal support and that can benefit from the speed and versatility of the Gemini 2.0 family.

DeepSeek

DeepSeek stands out due to its reasoning-oriented architecture, cost-efficiency, and open-source availability. It excels in technical areas such as coding and mathematics, making it 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 in conversations through increased factual accuracy, reduced hallucinations, and enhanced emotional intelligence. It is the best choice for applications that require a natural and engaging conversational experience, such as chatbots, virtual assistants, and creative writing.

Multimodality and open source: The trends of the next AI generation

Choosing the best model depends heavily on the specific use case and the user's priorities. Companies and developers should carefully analyze their needs and requirements and weigh the strengths and weaknesses of the various models to make the optimal choice.

The rapid development of AI models suggests that these models will continue to improve and evolve quickly. Future trends could include even greater integration of multimodality, enhanced reasoning capabilities, increased accessibility through open-source initiatives, and wider availability across various platforms. Ongoing efforts to reduce costs and increase efficiency will further drive the widespread adoption and application of these technologies across various industries.

The future of AI is not monolithic, but diverse and dynamic. Gemini 2.0, DeepSeek, and GPT-4.5 are just three examples of the diversity and innovative spirit that characterizes the current AI market. These models are expected to become even more powerful, versatile, and accessible in the future, fundamentally changing how we interact with technology and understand the world around us. The journey of artificial intelligence has only just begun, and the coming years promise even more exciting developments and breakthroughs.

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