What is the difference between an inference big model and an ordinary big model?

Core point: Don’t think of the inference big model as a simple upgraded version of the ordinary big model! These are two AI models based on different working mechanisms, training methods, and operating mechanisms.

The workflow of ordinary large models, such as ChatGPT and Qwen, is as follows: first pre-train with massive text data to let it learn language rules and various knowledge; then align through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).

In my own experience, general models like ChatGPT are very good at chatting and multi-round dialogues, but when faced with tasks that require step-by-step reasoning (such as debugging code), they sometimes give answers that seem right but are actually wrong. This made me realize that models are designed for different goals and can do very different things. Later, large reasoning models came out, such as OpenAI's o series, DeepSeek's R1, and Google's Gemini Flash Thinking. When dealing with problems such as mathematics and programming that require multi-step reasoning, they will "think" before answering.

Difference 1: Working mechanism

An ordinary large model is a bit like answering based on intuition. After receiving a question, it relies on what it has learned from pre-training to directly predict the most likely answer, pursuing speed and smoothness.

The reasoning model is different. It introduces the Long Chain of Thought (Long CoT). It does not simply add explanations to the output answers, but generates a very complex reasoning logic inside the model, similar to the draft paper we use when doing math problems. This process may include: breaking down complex problems into multiple steps, trying different problem-solving ideas, checking the correctness of the intermediate steps and correcting errors, and returning to the previous node when one path is blocked to find another way.

This Long CoT is the deep thinking process inside the model, and its length and complexity far exceed the CoT of ordinary models. It allows the model simulation to be closer to the way humans think when solving complex problems. When solving difficult problems, the reasoning model can invest more computing resources in exchange for higher accuracy by increasing the thinking time (i.e. generating a longer CoT).

Difference 2: Training Paradigm

The training focus of ordinary large models is to use SFT and RLHF to enable it to understand human language, give useful answers, and align with human values.

The training of large reasoning models focuses more on reinforcement learning (RL), especially reinforcement learning with verifiable rewards (RLVR). They are mainly trained on tasks where the correctness of answers can be clearly verified (such as whether math problems have standard answers, whether the code can pass test cases). After the model generates an answer, the correctness is verified through an automated program (rather than relying on human scoring), and the verification result is directly used as the reward signal of RL.

This training method has several advantages. First, it has a clear goal and directly optimizes the accuracy of the model in solving problems. Second, it avoids reward cheating and reduces the risk of the model generating seemingly reasonable but actually wrong answers in order to cater to human preferences or reward models. Third, it is highly scalable, and automated verification makes it possible to conduct larger-scale RL training, allowing the model to explore and learn more fully in the process of problem solving.

Difference 3: Core capabilities and application scenarios

It is precisely because of the differences in working mechanisms and training paradigms that the core capabilities of the two are different:

For ordinary large models, the strengths are language understanding and generation, wide knowledge coverage, and smooth and natural interaction. It is suitable for scenarios that require extensive knowledge and good communication skills, such as chatbots, content creation, information summarization, translation, and general question and answer.

For large reasoning models, their strengths lie in deep logical reasoning, complex problem solving, and high-precision calculations. They are particularly good at fields that require rigorous steps and deep thinking, such as mathematics, programming, scientific analysis, logical reasoning, and complex planning. Their performance on these tasks can often reach the level of experts, solving many difficult problems that traditional LLMs cannot achieve.

Taking DeepSeek's R1 series as an example, DeepSeek-R1-Zero demonstrated that reasoning capabilities (such as the use of long CoT) can emerge spontaneously through rule-reward-based RL training, but the model lacks general performance. DeepSeek-R1 uses multi-stage training (combining SFT, RL for reasoning and SFT, RLHF for general purposes), and finally obtains a balanced model that has both reasoning capabilities and good performance in general tasks and alignment. This shows that although RL is the core driver of reasoning capabilities, proper SFT guidance and general alignment training are equally important for building practical reasoning models.

How to choose?

Simply put, the inference big model is like a specialist, and the ordinary big model is like a general practitioner.

If you need to handle highly professional, logical reasoning, and high-precision tasks (such as performing complex scientific calculations, writing and verifying code, and solving Olympiad-level problems), a large reasoning model is the first choice.

If you need to build applications for conversations, write various documents, and provide information services, a regular large model will be able to meet your needs and will be more cost-effective, efficient, and applicable.

In the future, these two types of models may be further integrated, and a hybrid model with the advantages of both will emerge (it is predicted that one of the highlights of ChatGPT 5 will be the integration of the two). But now, only by understanding their differences and choosing the right model according to needs can AI be used well.