Can't we catch both the best performance and the best cost-effectiveness? (Problem) In mid-2025, OpenAI targeted the market by bifurcating its Reasoning model lineup into o1-pro and o3-mini. (Solution) Now developers can achieve optimal efficiency by selecting o1-pro for heavy scientific research and o3-mini for fast and repetitive coding tasks. (Evidence)

o1-pro: Uncompromising Intelligence

o1-pro thinks deeper using more computational resources than the existing o1 model.

• Use Cases: High-risk tasks where the 'correct answer' is crucial, such as drug discovery, complex legal analysis, and quantum physics simulation.
• Features: Hallucinations are extremely suppressed, and there are almost no logical leaps.

o3-mini: Balance of Speed and Intelligence

o3-mini is a small model specialized for science, math, and coding tasks.

• Advantages: Very fast and cheap. However, its reasoning capability exceeds the existing GPT 5.2 class.
• Usage: Real-time code autocompletion, math tutoring bots, data preprocessing agents.

Strategic Selection Guide

• Cost irrelevant, 100% accuracy goal -> o1-pro
• Large traffic, fast response, decent reasoning -> o3-mini
• General conversation and multipurpose -> GPT 5.2.1

OpenAI's portfolio is now ready to satisfy all needs.