From OpenAI’s O3 to DeepSeek’s R1: How Simulated Thinking Is Making LLMs Think Deeper

Large language models (LLMs) have evolved considerably. What started as simple text generation and translation tools are now used in research, decision-making and complex problem solving. An important factor in this shift is the growing capacity of LLMS to think more systematically by solving problems, evaluating multiple options and dynamically refining their answers. Instead of just predicting the next word in a series, these models can now perform structured reasoning, making them more effective in dealing with complex tasks. Leading models such as OpenAi’s O3, Google’s GeminiAnd the R1 of Deepseek integrate these possibilities to improve and analyze their ability to process information more effectively.

Understand simulated thinking

People naturally analyze different options before they make decisions. Whether it is a holiday or solving a problem, we often simulate different plans in our mind to evaluate several factors, weigh advantages and disadvantages and adjust our choices accordingly. Researchers integrate this ability to improve LLMs to improve their reasoning opportunities. Here, simulated thinking in essence refers to the ability of LLMS to perform systematic reasoning before you generate an answer. This is, in contrast to the easy collection of a response from stored data. A useful analogy is to solve a math problem:

• A base AI may recognize a pattern and quickly generates an answer without verifying it.
• An AI that uses simulated reasoning would go through the steps, check for errors and confirm the logic before it responds.

Chain of-Doving: Learning AI to think in steps

If LLMS thinks simulated if people have to perform, they must be able to break complex problems in smaller, successive steps. This is where the Chain of-Ducking (COT) Technology plays a crucial role.

COT is a designation approach that guides LLMS to go through methodically problems. Instead of jumping into conclusions, this structured reasoning process enables LLMs to divide complex problems into simpler, manageable steps and resolve step by step.

For example, when solving a word problem in mathematics:

• A Basic AI can try to match the problem with a previously considered example and give an answer.
• An AI who used the reasoning of a thought would sketch every step logically through calculations before he arrived at a definitive solution.

This approach is efficient in areas that require a logical deduction, multi-Step problem solution and contextual understanding. Although earlier models required by people supplied chains, advanced LLMs such as OpenAi’s O3 and the R1 of Deepseekek can apply adaptive learning and COT reasoning.

How leading LLMS simulated thinking implements

Different LLMs use simulated thinking in different ways. Below is an overview of how OpenAi’s O3, the models of Google DeepMind and Deepseek-R1 perform simulated thinking, together with their respective strengths and limitations.

OpenAI O3: Thinking as a chess player in advance

While exact details about the O3 model of OpenAI are unknown, researchers to believe It uses a technique that is comparable to Monte Carlo Tree Search (MCTS), a strategy used in AI-driven games such as Alfo. Just like a chess player who analyzes multiple movements before he decides, O3 investigates different solutions, evaluates their quality and selects the most promising.

In contrast to earlier models that depend on pattern recognition, O3 acts actively reasoning paths with the help of COT techniques. During the conclusion, it carries out extra computational steps to construct several reasoning chains. These are then assessed by an evaluation model – probably a reward model trained to guarantee logical coherence and accuracy. The final response was selected on the basis of a score mechanism to offer a well -reasoned output.

O3 follows a structured multi-step process. Initially it is refined on a huge data set of human reasoning chains, which internalizes logical thinking patterns. With inference time it generates multiple solutions for a certain problem, ranks on the basis of accuracy and coherence and refines the best if necessary. Although this method enables O3 to correct itself before they respond and improve accuracy, the consideration of computational costs of the processing of multiple possibilities requires a considerable processing power, making it slower and more resource-intensive. Nevertheless, O3 excels in dynamic analysis and problem solution and positions it under today’s most advanced AI models.

Google DeepMind: Refine answers as an editor

DeepMind has developed a new approach with the name ”Mind Evolution‘That reasoning deals with an iterative refinement process. Instead of analyzing several future scenarios, this model behaves more as an editor who refines different concepts of an essay. The model generates various possible answers, evaluates their quality and refines the best.

Inspired by genetic algorithms, this process ensures high quality reactions by iteration. It is especially effective for structured tasks such as logical puzzles and programming challenges, whereby clear criteria determine the best answer.

However, this method has limitations. Since it depends on an external scoring system to assess the response quality, struggling with abstract reasoning can be done without a clear or wrong answer. In contrast to O3, which dynamically argues in real time, the DeepMind model focuses on refining existing answers, making it less flexible for open questions.

DeepSeek-R1: Learning to reason as a student

Deepseek-R1 uses a weapon-based approach with which it can develop reasoning options in the course of time instead of evaluating several reactions in real time. Instead of trusting in advance generated reasoning data, Deepseek-R1 learns by solving problems, receiving feedback and improving iteratively comparable with how students refine their problem-solving skills through practice.

The model follows a structured learning loop of reinforcement. It starts with a basic model, such as Deepseek-V3, and is asked to solve mathematical problems step by step. Each answer is verified by means of direct code version, whereby the need for an extra model to validate correctness. If the solution is correct, the model is rewarded; If it is incorrect, it will be punished. This process is extensively repeated, so DeepSek-R1 can refine its logical reasoning skills and give more complex problems over time.

An important advantage of this approach is efficiency. In contrast to O3, which performs extensive reasoning during the inference time, DeepSek-R1 encloses reasoning options during training, making it faster and more cost-effective. It is very scalable because it does not require a massive labeled data set or a duration of verification model.

However, this approach -based approach has considerations. Because it depends on tasks with verifiable results, it excels in mathematics and coding. However, wrestling with abstract reasoning in law, ethics or creative problem solving can be done. Although mathematical reasoning can transfer to other domains, its wider applicability remains uncertain.

Table: Comparison between OpenAI’s O3, DeepMind’s Mind Evolution and Deepseek’s R1

The future of AI reasoning

Simulated reasoning is an important step to make AI more reliable and intelligent. As these models evolve, the focus will shift from the simple generation of text to developing robust problem -solving skills that look strongly like human thinking. Future progress will probably focus on making AI models that are able to identify and correct errors, integrate them with external tools to verify reactions and to recognize uncertainty when they are confronted with ambiguous information. However, an important challenge is the balancing of the reasoning depth with computational efficiency. The ultimate goal is to develop AI systems that carefully take into account their answers, which guarantees accuracy and reliability, just like a human expert who carefully evaluates every decision before they take action.