o3 is a solid choice for data analysis and mathematics, but ill-suited as a standalone chatbot or AI agent. It is best for technical teams that need a model to handle complex inputs (multimodal) and solve quantitative problems.

Strengths

Strong multimodal processing (text + complex images/charts)
Solid performance in quantitative problem-solving and mathematics
Strong capability in analyzing and synthesizing data across multiple sources

Weaknesses

Weak natural conversation—unsuitable for chatbots or conversational AI
Limited independent planning and autonomous execution (weak agent task performance)
Complex logical reasoning is not a strong suit

Use cases

Analyzing and extracting information from documents with images and tablesProblem-solving, data science challenges, and code reviewTasks requiring technical reasoning and data-driven logic

Guides & videos

The o3 model is accessible via ChatGPT on the web or the OpenAI API (API key required). Best for coding, mathematics, science, and tasks requiring deep reasoning—ask clear questions without instructing it to think step by step. o3 can use tools such as web search, file analysis, and image generation. Note: high-compute mode offers greater accuracy but higher cost; use low-compute for simpler tasks.

Reviews

An In-Depth Analysis of OpenAI's O3 Model and Its Comparative Performance ↗OpenAI o3 Benchmark Results: ARC-AGI 87.5%, SWE-bench 71.7%, and the Catch ↗