Large Language Models (LLMs) quickly transformed from a simple text prediction system to an advanced reasoning engine capable of coping with complex challenges. Originally designed to predict the next word in a sentence, these models have now facilitated solving mathematical equations, writing functional codes, and making data-driven decisions. The development of inference technology is a key driver behind this transformation, allowing AI models to process information in a structured and logical way. This article explores the reasoning techniques behind models such as Openai’s O3, Grok 3, DeepSeek R1, Google’s Gemini 2.0, and Claude 3.7 sonnet, highlighting their advantages and comparing their performance, cost, and scalability.
Inference techniques in large language models
To understand how the reasons for these LLMs are different, we first need to look at the different inference techniques these models are using. In this section, we propose four key reasoning techniques.
- Inference time calculation scaling
This technique improves model inference by allocating additional computing resources during the response generation phase without changing the core structure of the model or retraining it. It allows the model to “think harder” by generating multiple potential answers, evaluating them, or refining its output through other steps. For example, when solving complex mathematical problems, the model may break it down into smaller parts and work through each part in turn. This approach is particularly useful for tasks that require deep thinking, such as logical puzzles or complex coding challenges. Although it improves the accuracy of response, the technology also leads to higher runtime costs and slower response times, making it suitable for applications where accuracy is more important than speed. - Pure reinforcement learning (RL)
In this technique, the model is trained through trial and error by rewarding correct answers and punishing errors. The model interacts with the environment, such as a set of questions or tasks, and learns by adjusting its strategies based on feedback. For example, when in charge of writing code, the model may test various solutions, and if the code is executed successfully, it will be rewarded. This approach mimics how a person learns the game through practice, enabling the model to adapt to new challenges over time. However, pure RL can be computationally required and sometimes even unstable, as the model may find shortcuts that do not reflect real understanding. - Pure Supervised Fine Tuning (SFT)
This method enhances inference by training models based on high-quality label datasets created only by humans or more powerful models. The model learns to copy the correct inference pattern from these examples, making it effective and stable. For example, to improve its ability to solve equations, the model might study the set of problems solving, learning follows the same steps. This approach is simple and cost-effective, but depends to a large extent on the quality of the data. If these examples are weak or limited, the performance of the model may be affected and may struggle with tasks outside of the training range. Pure SFT is best suited to clearly defined issues where clear, reliable examples are available. - Reinforcement learning with supervised fine-tuning (RL+SFT)
This method combines the stability of supervised fine-tuning with adaptability to enhanced learning. The model first received supervised training on the labeled dataset, which provides a solid knowledge base. Then, strengthening learning helps to improve the problem-solving skills of the model. This hybrid approach balances stability and adaptability, providing effective solutions to complex tasks while reducing the risk of behavioral instability. However, it requires more resources than purely supervised fine-tuning.
Leading LLM’s reasoning method
Now, let’s look at how to apply these inference techniques in leading LLMs such as 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 inference time calculation scaling to enhance its inference. By dedicated additional computing resources during response generation, O3 is able to provide highly accurate results on complex tasks such as advanced mathematics and coding. This approach allows O3 to perform well on benchmarks such as ARC-AGI testing. However, it is based on higher inference costs and slower response times, making it best suited for applications where accuracy is critical, such as research or technical problem solving. - Xai’s Grok 3
Grok 3 developed by XAI combines inference time computation scaling with professional hardware, collaborative processors for tasks such as symbolic mathematical manipulation. This unique architecture allows Grok 3 to process large amounts of data quickly and accurately, making it very effective for real-time applications such as financial analysis and real-time data processing. Although the Grok 3 provides fast performance, its high computing needs can increase costs. It performs well in environments where speed and accuracy are critical. - DeepSeek R1
DeepSeek R1 initially used pure reinforcement learning to train its model, allowing it to develop independent problem-solving strategies through trial and error. This enables DeepSeek R1 to adapt and be able to handle unfamiliar tasks such as complex math or coding challenges. However, pure RL can lead to unpredictable outputs, so DeepSeek R1 incorporates supervised fine-tuning in the later stages to improve consistency and coherence. This hybrid approach makes DeepSeek R1 a cost-effective application with priority over polishing response flexibility. - Google’s Gemini 2.0
Google’s Gemini 2.0 uses a hybrid approach that may combine inference time computation scaling with enhanced learning to enhance its inference capabilities. The model is designed to handle multimodal inputs such as text, images, and audio while being excellent in real-time inference tasks. Its ability to process information before response ensures high accuracy, especially in complex queries. However, like other models that use inference time scaling, the operation of Gemini 2.0 can be expensive. It is an application that requires reasoning and multimodal understanding, such as an interactive assistant or data analysis tool. - Claude of Humanity 3.7 Sonnets
Claude 3.7 sonnet from anthropomorphic integration of inference time calculation scaling, focusing on security and alignment. This allows the model to perform well in tasks that require accuracy and interpretation, such as financial analysis or legal document review. Its “extended thinking” pattern allows it to adjust its reasoning work, giving it a quick and in-depth problem solution. Although it provides flexibility, users must manage the tradeoff between response time and depth of reasoning. Claude 3.7 sonnets are particularly suitable for regulated industries where transparency and reliability are crucial.
Bottom line
The transition from basic language models to complex inference systems represents a significant leap in AI technology. By leveraging inference time to compute scaling, pure reinforcement learning, RL+SFT and pure SFT, such as OpenAi’s O3, Grok 3, DeepSeek R1, Google’s Gemini 2.0, and Claude 3.7 SONNET becomes better at solving complex, real-world problems. From O3’s intentional problem-solving to DeepSeek R1’s cost-effective flexibility, each model’s inference approach defines its advantages. As these models continue to evolve, they will unlock new possibilities for AI, making it a more powerful tool to solve real-world challenges.
