OpenAI Releases o3-mini Chain‑of‑Thought: First Tests, Community Reactions, and Critical Analysis

OpenAI announced that both free and paid users can now view the reasoning process (chain‑of‑thought) of the o3‑mini model, marking a rare public exposure of an LLM's internal thought flow.

Users noted that the model humorously responded to a question about why today isn’t Friday, using Zeller’s congruence and a leap‑year check before delivering a witty answer.

Community commentary highlighted concerns that the displayed CoT might be a post‑processed summary rather than raw data, with developers like Kevin Weil and Mckay Wrigley warning that such summaries could obscure errors or reduce debugging usefulness.

In experimental tests, researchers prompted o3‑mini to play tic‑tac‑toe, observing that it generated a plausible optimal move and produced visual output, though its reasoning was described as “a bit unreliable.”

Another test asked the model to count the number of “r” letters in the word “strawberry”; o3‑mini explained why such a simple task can be challenging for large language models, citing natural‑language training, tokenization issues, lack of iterative reasoning, and reliance on pattern recognition.

Further community scrutiny suggested that the publicly shown CoT might be altered, noting discrepancies in token counts between o3‑mini‑high and other models, and questioning the speed of the displayed reasoning.

TechCrunch reported that OpenAI confirmed the released CoT was post‑processed to remove unsafe content and to provide a more user‑friendly experience for non‑English speakers, implying the original Chinese‑language reasoning was not shown.

The article concludes with references to the original sources and a promotional note about an upcoming AI Agent training camp.