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OpenAI Full Guide to Escalation Model (2025)

o1, o3, o4-mini: The path from thinking to action

What's a model of reasoning?

OpenAI 's o-series models are designed for use in Complex reasoning. the AI model. Unlike the traditional GPT model, o series of models are enhanced learning training that takes place before response. More time to think inside. 。 This process of thinking is called the "Chain of Thinking" Thought) allows the model to try different solvers, check errors and correct logic internally before giving the final answer.

Core idea: expansion in reasoning (Test-Time Scaling)

Traditional models are mainly enhanced by increasing training data and parameters (extension during training), while o The series of models created a new paradigm of “extension in reasoning” - allowing the model to devote more computing resources at the reasoning stage. This means that the more difficult the problem, the longer the model can think, the more accurate the answer.

📅 Models into timeline
September 2024
o1-preview & o1-mini release
The first time I introduced the concept of reasoning models, I showed AI cross-cutting progress in mathematics and programming.
December 2024
o1 Official & o3 Preview
O1 Full Open, o3 achieved an impressive 87.5% score in ARC-AGI tests, updating world records.
April 2025
o3 & o4-mini Officially Published
For the first time, the “image thinking chain” was achieved to support multi-modular reasoning, autonomous use of tools and more efficient calculus distribution.
🚀 2025 Comparison of mainstream reasoning models
o3 (Flagship Model)

The most intelligent model to date has the reasoning of a “genius level”. Supports the image think-through chain, which enables you to call as autonomously as an intelligent body.

96.7% AAME Math
87.5% ARC-AGI
71.7% SWE-bench
o4-mini (efficient model)

Optimizing quick, low-cost reasoning. Although the parameters are small, they maintain a strong logical capacity and image reasoning support.

Speed Response speed
Support Image Thinking
Extremely Low API Cost
• Detailed comparison of parameters
Feature o1 (classical) o3 (flagships) o4-mini (efficient)
Date of issue 2024.12 2025.04 2025.04
Image Thinking Chain Limited support Full Support Full Support
Tool Call Partially supported Full Support Full Support
Typical reflection time. 10-30 seconds 10-60 seconds 1-5 seconds
Population applicable Enterprise production environment Pro/Scientific Users All Free/API Users

Depth resolution: Why is the reasoning model the way to AGI?

Over the last few years, the progress of the Big Language Model (LLM) has been largely dependent on the Scaling Law of Scale. Laws) is to improve performance by investing more training data and computing. However, simple model matching cannot solve truly complex logical problems. OpenAI o series model The emergence of the AI paradigm represents a shift from “quick reaction” to “slow thinking”.

1. Expansion of reasoning: empowering AI to resolve unknown difficulties

Traditional GPT-4o Like an absconding but reckless genius who can answer most of the questions quickly, but when faced with a mathematical dilemma or complex code logic that has never been seen, it is easy to hallucinate without thought. The reasoning model is like a rigorous scientist who does multiple rounds of self-playing and routing before answering. This “expanded” ability to AI is facing new problems that never appear in training centres (e.g. ARC-AGI) When tested, the answer can still be found through purely logical extrapolations.

2. Image thinking chain: AI Second Revolution of Visual Understanding

O3 and o4-mini introduced in 2025 Image Thinking Chain (Image COT) 。 This is not just an AI "see" map, but an AI "think" map. For example, when you upload a complex circuit map, AI It is not simply a list of the elements, which analyzes current flow, calculates the resistance fraction pressure and extrapolates the expected function of the circuit. This vision is a deep combination of logic, so... AI has real potential to solve real-life physical world problems.

3. Addressing specific issues: operational applications of reasoning models

  • Scientific research and development: Using an extremely strong logical push capability, assistance is provided in drug molecular simulation, protein folding analysis or complex physical laboratory design.
  • Software architecture design: O3 Yes The performance of SWE-bench (the software engineer benchmark test) has surpassed that of many mid-level programmers. It understands the dependency of the entire code library and autonomously completes the module migration and re-engineering.
  • High-precision education: Compared to normal AI only give answers, o Series models provide detailed thinking processes that help students understand “why” rather than just one result.

4. Costs and choices: Which should you use?

Although extremely powerful, the process of thinking requires a huge amount of effort, and the cost of a single complex mission may be higher. o4-mini The appearance of a perfect balance of this. It provides an excellent value for money for day-to-day development, medium-cost logical analysis. Thousands Recommendation: Use in creative writing or in simple questions and answers GPT-4o； When conducting logical extrapolation, mathematical proof or code debugging, give priority to the o series model.

Thousands will keep you up to date with OpenAI reasoning models. At 2025. Yearly, the use technique of the reasoning model will be the core competitiveness of every user of AI.