How to choose the most suitable ChatGPT model for you?GPT-4o, o3-mini and other models detailed explanation and application strategy

With the rapid advancement of Artificial Intelligence (AI) technology, OpenAI continues to release more and more ChatGPT models, bringing users more powerful tool options such as GPT-4o, GPT-4o Mini, o1, and o3-mini etc. Faced with such a wide range of models, users and enterprises are faced with a key question: how to choose the most appropriate version in order to get the most accurate results in the shortest time and maximize the potential of AI?

In this article, we will analyze the selection of ChatGPT models, compare the differences between GPT-4o and o3-mini, and reveal the mystery of "inference models". Based on the official guide released by OpenAI, the article will systematically sort out the characteristics of each model and provide practical strategies to help readers work with ChatGPT more efficiently and unleash the powerful performance of AI.

GPT Series vs. o-Series Models: Differences and Selection Guide

To help readers quickly understand the differences between different GPT series (e.g., GPT-4o, GPT-4o Mini) and o-series (e.g., o1, o3-mini, o3-mini-high) models, we have compiled the following comparison table:

Currently, the most widely used model is GPT-4o. If lower latency and instant feedback are sought, and the task is relatively simple, GPT-4o Mini will be the ideal choice. And when facing challenging problems that require highly complex reasoning and deep analysis, inference models such as o1, o3-mini, and o3-mini-high provide more specialized support.

In addition to the functional differences described above, users need to consider the following when choosing a model(manufacturing, production etc) costscap (a poem) API Call Methods. In general, more powerful models, such as inference models, have relatively high API call costs. In addition, different models may have differences in API access methods, rate limits, and so on. When choosing a model, users are advised to consider their own needs, budget, and technical capabilities to choose the most suitable model. OpenAI's official website usually provides detailed model pricing and API documentation, making it easy for users to compare and select models.

What is a reasoning model? How does the "chain of thought" work?

Reasoning Models are AI models developed by OpenAI specifically for solving complex problems that require multi-step reasoning and deep analysis.

The core mechanism of the reasoning model is the Chain of Thought. Simply put, the model performs a series of internal reasoning steps before giving an answer, performing multiple levels of thinking and calculations, and ultimately arriving at a conclusion. This mechanism enables the reasoning model to effectively handle complex and ambiguous information and provide more accurate answers through a deeper reasoning process.

In order to visualize how the "chain of thought" works, we can use a simple example. Suppose we ask the model: "Why is the sky blue?"

• Non-inferential modeling: A direct and conclusive answer may be given, such as "Because of Rayleigh scattering." This answer is succinct, but lacks a deeper explanation of the problem.
• Reasoning model (based on chain of thought): Then a series of inference steps are performed:
  1. Knowledge Retrieval: The model first searches its knowledge base for information about "sky" and "blue" and finds the concept of "Rayleigh scattering".
  2. Principle Analysis: The model will further analyze the principle of Rayleigh scattering, understanding that it refers to the phenomenon of light being scattered by tiny particles, with the intensity of scattering being inversely proportional to the fourth power of the wavelength.
  3. Wavelength and color correlation: The model associates wavelengths with colors, knowing that blue light has shorter wavelengths and red light has longer wavelengths.
  4. Drawing conclusions: Taking all these analyses together, the model finally concludes: "The sky is blue because the molecules and tiny particles in the air scatter various colors of sunlight, and blue light has a shorter wavelength and is more easily scattered than red light, so the sky appears blue."

As you can see from the above steps, the "chain of thought" enables the reasoning model to act like a human being and to performDecomposition, analysis and reasoningThis will provide deeper and more comprehensive answers, rather than just scratching the surface.

Application scenarios for inference modeling: addressing complex challenges

Reasoning models are particularly good at dealing with complex problems that require multi-step reasoning and comprehensive analysis, and show excellent advantages in "coping with complex situations". Below are some typical application scenarios of inferential modeling:

1. Legal and Financial Analysis: In the legal and financial fields, inference modeling can quickly process massive amounts of unstructured data and extract key information from it. For example, it can analyze obscure clauses in contracts, assess hidden risks in financial statements, and provide expert advice to assist professionals in decision-making. In addition, inference modeling can be applied toLegal consulting, case studies, tax planning, investment analysisIt helps legal and financial professionals to improve the efficiency and quality of their work and decision-making in a variety of scenarios.
2. Medical diagnosis and scientific research: The medical and scientific research fields usually involve huge amounts of data and complex analysis processes. Reasoning models can quickly extract key information from large amounts of medical data to assist doctors in making more accurate diagnoses; in scientific research, reasoning models can help researchers discover valuable research trends and patterns from massive amounts of data to accelerate the research process. For exampleGenomics research, drug discovery, disease prediction, clinical decision support Inferential modeling has shown great potential in many areas.
3. Corporate strategic planning and project management: Reasoning models can be used to analyze internal and external data to assist managers in multi-step decision-making, such as market trend forecasting, competitor analysis, risk assessment, etc., so as to help companies make smarter strategic plans, improve project management, and ultimately achieve business goals. In addition, inference modeling can also be applied toSupply chain optimization, customer relationship management, product innovation, human resource management and other aspects of business operations.

6 Tips from OpenAI to Improve Efficiency with Inference Models

Inferential models think differently than generalized models, and therefore, there are some techniques that need to be adapted when using inferential models. OpenAI provides the following 6 practical tips for using inference models to help users work more efficiently with inference models:

1. Keep your instructions simple and clear: Reasoning models excel at handling concise and clear instructions. By avoiding overly complex statement structures and keeping the problem description simple, the model can more accurately understand the user's intent and quickly provide an answer. The clearer the instructions, the less likely the model is to be ambiguous, thus increasing the accuracy of the answers.
2. Provide clear guidelines: If the user has specific constraints or requirements, such as budget constraints, time frames, etc., make sure they are clearly expressed in the prompt. This will help the reasoning model to more accurately scope the answer and generate a solution that better meets the user's needs. Clear guidelines can help models focus on key information and avoid wasting computational resources on irrelevant information. (Prompt, also often referred to as "prompt" or "instruction.")
3. Clearly define the end goal: When designing a prompt, it is important to clearly describe the desired outcome. This not only helps the reasoning model to understand the user's needs, but also guides the model to adjust its internal reasoning process to produce an answer that better meets the expectations. A clear description of the goal is the basis for effective reasoning in the model.
4. Avoid explicitly instructing thinking steps: Since the reasoning model itself has strong internal reasoning ability, users do not need to ask the model to think step by step. Prompting too many thinking steps may interfere with the normal reasoning process of the model, reducing processing efficiency and even affecting the accuracy of the final result. Too much intervention may instead limit the creativity and autonomy of the model.
5. Utilize separators to enhance information differentiation: When the input data is complex, separators such as Markdown syntax, XML tags or headings can be used to clearly distinguish different parts of the information. This helps the model understand and process complex data more accurately and improves the precision of information processing. Delimiters are like structured information for the model to help it better organize and understand the input.
6. Prioritize attempts to not provide examples, then add examples in small amounts as appropriate: Inference models can reason effectively even without examples. Therefore, it is recommended that users first try to provide no examples and let the model generate answers based solely on the question itself. If the initial results are not satisfactory, then provide examples in small amounts to guide the model to better understand the user's intent and optimize the output according to the specific needs. A small number of examples can help the model understand a user's specific preferences or the specific requirements of a task, but too many examples may limit the model's ability to generalize.

When GPT-5 comes out, model selection will no longer be a problem?

With the release of GPT-5 approaching, OpenAI expects to further simplify the model selection process for users. Sam Altman, CEO of OpenAI, said that GPT-5 will integrate the advantages of the GPT series of models and inference models to realize automatic model selection and switching, and the system will intelligently select the most suitable model for processing according to the type and complexity of the task proposed by the user. In this way, enterprises and developers will no longer need to manually select models, thus significantly improving work efficiency and greatly simplifying the development process of AI applications, making the application of AI technology more popular and convenient.

Knowledge Points.

• Model selection depends on the task complexity: For simple tasks that require a fast response, a non-inference model such as GPT-4o or GPT-4o Mini is sufficient. For tasks that require deep analysis and complex reasoning, inference models such as o1, o3-mini, etc. should be chosen.
• Understand the importance of the "chain of thought": "Chain of Thought is the core mechanism of an inference model, which makes the model more powerful for problem solving. Understanding the "chain of thought" helps users to better utilize the inference model and design more effective prompts.
• Master the skill of working with reasoning models: OpenAI provides 6 tips to help users work more efficiently with inference models and improve the quality and efficiency of their outputs.
• Focus on modeling trends: With the introduction of more advanced models such as GPT-5, the selection and use of AI models will become more intelligent and convenient. Users should keep an eye on the development trend of AI technology so that they can better apply the latest AI tools to improve their work efficiency and innovation.