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

LLMS models develop rapidly from simple textual prediction systems to advanced thinking engines capable of facing complex challenges. These models are initially designed to predict the following word in the sentence, and have now advanced to solve mathematical equations, write functional code, and make decisions that depend on data. The development of thinking techniques is the main driver behind this transformation, allowing artificial intelligence models to process information in an organized and logical way. This article explores thinking techniques behind models such as Openai’s O3, Grok 3, Deepseek R1, Google Gemini 2.0 and Claude 3.7 Sonnet, with highlighting their strengths, comparing their performance, cost and expansion.

Thinking techniques in large language models

To find out how this LLMS causes differently, we first need to consider the various thinking techniques that these models use. In this section, we offer four main thinking techniques.

• Timing time in reasoning
  This technology improves the model thinking by customizing additional mathematical resources during the response of the response, without changing the basic structure of the model or re -trains it. It allows the model to “think hard” by creating, evaluating multiple answers, or improving it through additional steps. For example, when solving the problem of complex mathematics, the model may be divided into smaller parts and works through each one sequentially. This approach is especially useful for tasks that require deep and thoughtful thinking, such as logical puzzles or complex coding challenges. While it improves the accuracy of responses, this technique also leads to high operating time costs and slower response times, making it suitable for applications where accuracy is more important than speed.
• Learn pure reinforcement (RL)
  In this technique, the model is trained on the mind through experience and error through the reward for the correct answers and punishing errors. The model interacts with an environment – such as a set of problems or tasks – and is learned by controlling its strategies based on the comments. For example, when assigned to a thick, the model may test different solutions, and earn a reward if the code is successfully performed. This approach mimics how a person learns a game through training, allowing the model to adapt to new challenges over time. However, the pure RL can be mathematical and sometimes stable, because the model may find shortcuts that do not reflect a real understanding.
• Sincere polishing (SFT)
  This method enhances logic by training the model only on high -quality data sets, and is often created by humans or stronger models. The model learns to repeat the correct thinking patterns of these examples, making them effective and stable. For example, to improve its ability to solve equations, the model may study a set of problems that have been solved, and learn to follow the same steps. This approach is clear, direct and effective, but it relies heavily on data quality. If the examples are weak or limited, the model’s performance may suffer, and he may suffer from tasks outside the range of training. SFT Pure is more suitable for well -specific problems as clear and reliable examples are available.
• Learning reinforcement with supervision control (RL+SFT)
  This approach combines the stability of control over supervision with the ability to adapt to reinforcement learning. Models are first subject to a subject to overseeing the named data sets, which provides a strong basis for knowledge. Next, learning reinforcement helps improve problem solving skills in the model. This hybrid method works to balance stability and the ability to adapt, providing effective solutions to complex tasks while reducing the risk of wrong behavior. However, it requires more resources than pure pure control.

Logic methods in driving llms

Now, let’s study how these thinking 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 is used primarily to limit the time of reasoning time to enhance his thinking. By customizing additional mathematical resources while generating the response, O3 is able to achieve very accurate results on complex tasks such as advanced mathematics and coding. This o3 approach allows exceptionally well on standards such as the ARC-EAGI test. However, it comes at the cost of high inferences and response times is slower, which makes them more suitable for applications that are decisive, such as searching or solving technical problems.
• Xai’s Grok 3
  Grok 3, which was developed by Xai, combines the scaling of the time of inferring time with specialized devices, such as shared processors for tasks such as symbolic sporting manipulation. This unique GROK 3 structure allows large quantities of data quickly and accurately, making it very effective for actual time applications such as financial analysis and direct data processing. While GROK 3 offers quickly, its high mathematical requirements can increase costs. It excels in environments where speed and accuracy are of utmost importance.
• Deepsek R1
  Deepsek R1 is initially used to learn pure reinforcement to train his model, allowing it to develop independent strategies to solve problems through experience and error. This makes Deepseek R1 to be adaptable and able to deal with unfamiliar tasks, such as math challenges or complex coding. However, pure RL can lead to unexpected outputs, so Deepseek R1 includes a controlled control process in subsequent stages to improve consistency and cohesion. This hybrid approach makes Deepseek R1 an effective option for applications that give priority to flexibility on polished responses.
• Google’s Gemini 2.0
  Gueini 2.0 of Google uses a mixed approach, which is likely to combine the limitation of the time of reasoning with reinforcement learning, to enhance thinking capabilities. This model is designed to deal with multimedia inputs, such as text, photos and sound, with excellence in the tasks of actual time. Its ability to process information before responding ensures high accuracy, especially in complex queries. However, like other models that use the time of reasoning time, Gemini 2.0 can be expensive to work. It is ideal for applications that require multimedia understanding and understanding, such as interactive assistants or data analysis tools.
• Claude 3.7 Sonit Anthrop
  Claude 3.7 Sonatat from the anthropoor merges a sense of reasoning time with a focus on safety and alignment. This model enables well in tasks that require both accuracy and explanation, such as financial analysis or legal documents review. It allows his “extended thinking” mode to control thinking efforts, making it multi -use for both rapid and in -depth problems. Although it provides flexibility, users must manage the comparison between response time and depth of thinking. Claude 3.7 Sonnet is especially suitable for organized industries where transparency and reliability are two decisions.

The bottom line

The shift from basic language models to advanced thinking systems is a great leap forward in artificial intelligence technology. By taking advantage of techniques such as scaling time in reasoning, learning pure reinforcement, RL+SFT, and pure SFT, models such as Openai’s O3, GROK 3, Deepseek R1, Google’s Gemini 2.0 and Claude 3.7 have become more skilled in solving complex problems in the real world. The approach of each model in thinking defines its strengths, starting from solving the deliberate problems of O3 to the effective Deepsek R1 flexibility. As these models continue to develop, you will lock new possibilities for Amnesty International, making it a more powerful tool for addressing challenges in the real world.