How OpenAI’s o3, Grok 3, DeepSeek R1, Gemini 2.0, and Claude 3.7 Differ in Their Reasoning Approaches

Large language models (LLMS) quickly evolve from simple text prediction systems to advanced reasoning engines that are able to meet complex challenges. Initially designed to predict the following word in a sentence, these models have now improved for solving mathematical comparisons, writing functional code and making data -driven decisions. The development of reasoning techniques is the most important motivation behind this transformation, so that AI models can process information in a structured and logical way. This article investigates the reasoning techniques behind models such as OpenAi’s O3, Grok 3Deepseek R1, Google’s Gemini 2.0And Claude 3.7 SonnetEmphasizing their strengths and comparing their performance, costs and scalability.

Reasoning techniques in large language models

To see how these LLMS reason differently, we must first look at various reasoning techniques that these models use. In this section we present four important reasoning techniques.

• Inference time calculation scaling
  This technique improves the reasoning of the model by assigning extra computational sources during the phase of generating the response, without changing the core structure of the model or to train. This allows the model “to think harder” by generating multiple potential answers, evaluating or refining the output by refining extra steps. When solving a complex mathematics problem, for example, the model can split it into smaller parts and continue to work any successively. This approach is particularly useful for tasks that require deep, deliberate thoughts, such as logical puzzles or complicated coding challenges. Although it improves the accuracy of the reactions, this technique also leads to higher runtime costs and slower response times, making it suitable for applications where precision is more important than speed.
• Pure reinforcement Learning (RL)
  In this technique, the model is trained by trial and error by rewarding the correct answers and punishing mistakes. The model interacts with an environment – such as a series of problems or tasks – and learns by adjusting its strategies based on feedback. For example, when the task is to write code, the model can test different solutions and earn a reward if the code is successfully performed. This approach mimics how a person learns a game by practicing, so that the model can adapt to new challenges over time. However, Pure RL can be computational demanding and sometimes unstable, because the model can find shortcuts that do not reflect a real understanding.
• Pure supervised fine-tuning (SFT)
  This method improves reasoning by only training the model on high -quality labeled data sets, often made by people or stronger models. The model learns to replicate the right reasoning patterns from these examples, making it efficient and stable. For example, in order to improve his ability to resolve comparisons, the model can, for example, study a collection of solved problems and learn to follow the same steps. This approach is simple and cost -effective, but is highly dependent on the quality of the data. If the examples are weak or limited, the performance of the model can suffer and struggle with tasks outside of his training range can be done. Pure SFT is most suitable for well -defined problems where clear, reliable examples are available.
• Learning reinforcement with guided refinement (RL+SFT)
  The approach combines the stability of controlled refinement with the adaptability of learning to reinforce. Models first undergo guided training on labeled data sets, which offers a solid knowledge base. Subsequently, learning strengthening helps to refine the problem -solving skills of the model. This hybrid method balances stability and adaptability and offers effective solutions for complex tasks, while the risk of whimsical behavior is reduced. However, it requires more resources than pure guided refinement.

Reasoning approaches in leading LLMS

Let us now investigate how these reasoning techniques are applied in the leading LLMS, including OpenAi’s O3, Grok 3, Deepseek R1, Google’s Gemini 2.0 and Claude 3.7 -Sonnet.

• OpenAi’s O3
  OpenAi’s O3 mainly uses information about inference time to improve the reasoning. By devoting extra computational sources while generating reactions, O3 can produce very accurate results in complex tasks such as advanced mathematics and coding. With this approach, O3 can perform exceptionally well on benchmarks such as the ARC-AGI TEST. However, it is at the expense of higher conclusion costs and slower response times, making it best suitable for applications where precision is crucial, such as research or technical problem solution.
• Xai’s Grok 3
  Grok 3, developed by Xai, combines inference time calculation with specialized hardware, such as co-processors for tasks such as symbolic mathematical manipulation. With this unique architecture, Grok can process 3 large amounts of data quickly and accurately, making it very effective for real -time applications such as financial analysis and live data processing. Although Grok 3 offers rapid performance, the high computational requirements can increase the costs. It excels in environments where speed and accuracy are of the utmost importance.
• Deepseek R1
  Deepseek R1 initially uses pure reinforcement learning to train its model, so that it can develop independent problem -solving strategies by traps and errors. This makes Deepseek R1 adjustable and able to handle unknown tasks, such as complex mathematics or coding challenges. However, Pure RL can lead to unpredictable outputs, so deep R1 contains under supervision in later stages to be supervised to improve consistency and coherence. This hybrid approach makes Deepseek R1 a cost -effective choice for applications that prioritize flexibility over polished reactions.
• Google’s Gemini 2.0
  Google’s Gemini 2.0 uses a hybrid approach, which probably combines a combination of inference-time calculation with reinforcement education to improve the reasoning options. This model is designed to process multimodal inputs, such as text, images and audio, while they excel in real -time reasoning tasks. The ability to process information before they respond ensures high accuracy, especially in complex questions. However, just like other models using inference time scaling, Gemini 2.0 can be expensive to work. It is ideal for applications that require reasoning and multimodal understanding, such as interactive assistants or tools for data analysis.
• Anthropic’s Claude 3.7 Sonnet
  Claude 3.7 Sonnet from Anthropic integrates inference time calculation with a focus on safety and coordination. This enables the model to perform well in tasks that require both accuracy and statements, such as financial analysis or legal document assessment. The “Extended Thinking” mode enables it to adjust its reasoning efforts, making it versatile for both fast and in -depth problem solving. Although it offers flexibility, users must manage the assessment between the response time and the depth of the reasoning. Claude 3.7 Sonnet is especially suitable for regulated industries where transparency and reliability are crucial.

The Bottom Line

The shift from basic models to advanced reasoning systems is a big leap forward in AI technology. By using techniques such as inference-time compute scaling, pure reinforcement learning, RL+SFT and Pure SFT, models such as OpenAI’s O3, Grok 3, Deepseek R1, Google’s Gemini 2.0 and Claude 3.7 Sonnet are more skilled in solving complex, real-world-problems. The reasoning of each model defines its strengths, from the intentional problem solution of O3 to the cost -effective flexibility of Deepsek R1. As these models continue to evolve, they will unlock new possibilities for AI, making it an even more powerful tool to take on realistic challenges.