Why does GPT5 Thinking create a hard-to-read plan?

Understanding Why GPT-5 Thinking Produces Complex and Hard-to-Read Plans

In the ongoing evolution of AI language models, users often encounter variations in the clarity and readability of generated content. A common observation is that when instructing models like GPT-5 in “Thinking” mode to outline plans or ideas, the responses tend to become excessively detailed, structured, and sometimes cumbersome to interpret. This article explores why GPT-5’s “Thinking” mode generates such intricate plans, compares it with other modes of output, and discusses the implications for users seeking clear, digestible content.

The Phenomenon of Overly Detailed Plans in GPT-5 Thinking Mode

Imagine requesting GPT-5 to “suggest topics for writing five articles for my travel blog.” When using GPT-5’s standard “Thinking” mode, the output may resemble:

“1. 12-Day Aegean Ruins Route by Public Transport (Turkey) – Working title: “Aegean Archeology by Dolmuş: 12 Days from Bodrum to Bergama” …”

This plan includes an elaborate breakdown: a proposed route, key sections, target keywords, and specific tips—far more information than a typical simple list.

Why does this happen?
GPT-5’s “Thinking” mode aims to produce comprehensive, well-structured plans that analyze multiple aspects of a task. It tends to generate detailed outlines with subsections, key points, and considerations to ensure thoroughness. While valuable in complex scenarios, this verbosity can hinder quick comprehension and clutter the response with excessive details.

Comparing Outputs: GPT-5 Instant vs. Claude Sonnet 4.5 Extended Thinking

Other modes and models tend to produce more natural, more concise responses:

• GPT-5 Instant: Focuses on quick, coherent ideas, delivering succinct article titles and themes without overwhelming detail.
• Claude Sonnet 4.5 Extended Thinking: Despite its expanded approach, it produces structured yet naturally flowing content that resembles human writing, emphasizing clarity over depth.

For example, in the travel blog context, the GPT-5 Instant might suggest:

“Hidden Gems: Underrated Cities in Europe Worth Visiting”

Whereas GPT-5 Thinking provides an intricate plan involving multiple segments, detailed route suggestions, and specific keywords.

Is More Detail Always Better?

While comprehensive plans may seem more useful because of their depth, they can also become unwieldy. The additional detail and structure might be valuable