Claims Library Entry
Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it)
GPT-5 uses a router that independently controls reasoning depth and output length, defaulting to cost-optimized settings that produce shallow or bloated results. The article explains how explicit phrases like 'Think hard about this' force deeper reasoning, while specifying exact word counts or paragraph ranges controls verbosity. Together, these two instruction types override the router defaults for better outputs.
Published October 1, 2025 by Kamil Banc
Lead claim
GPT-5's router defaults to shallow, cost-optimized settings—explicit phrases force deeper reasoning and controlled output length.
Atomic Claims
What this article supports
Copy individual claims as needed.
Claim 1 · Source summary
Precision Prompting Shift
GPT-5 follows precise instructions better than previous models but performs worse when prompts are vague.
Claim 2 · Source summary
Router Controls Two Dials
GPT-5 routes every request through a router that independently selects reasoning depth and output verbosity.
Claim 3 · Source summary
Cost-Optimized Defaults
The router defaults to low reasoning and medium verbosity because those settings are cheapest to run.
Claim 4 · Kamil's interpretation
Reasoning Trigger Phrases
Explicit phrases like 'Think hard about this' force high reasoning, while vague emphasis like 'This is critical' fails.
Claim 5 · Kamil's interpretation
Explicit Output Boundaries
Setting exact word counts, such as 100 words or 600 to 800 words, controls verbosity.
Evidence
Context behind the claims
Quote
"GPT-5 flipped this. It follows precise instructions better than any previous model but performs worse when you're vague."
Key statistics
100 words or less
Recommended compressed output length for executive updates that senior people scan in under 30 seconds
3 to 5 paragraphs
Recommended standard briefing length for team coordination where colleagues need causation context
600 to 800 words
Recommended comprehensive documentation length for reference materials used by multiple teams over weeks
Supporting context
The article presents a practitioner framework based on Kamil Banc's analysis of GPT-5's architectural changes, specifically its router system that makes independent decisions about reasoning depth and verbosity. The methodology relies on testing specific instruction phrases against the router's cost-optimized defaults, distinguishing between explicit cognitive commands that work and vague emphasis phrases that fail. Practitioners can apply this by adding reasoning triggers like 'Think hard about this' for high-stakes decisions such as financial planning or risk evaluation, while specifying exact word counts matched to audience needs. The author acknowledges a trade-off: forcing higher reasoning increases processing time and token cost, so it should be reserved for situations where getting it wrong once costs more than the compute. The framework is framed as practitioner experience rather than peer-reviewed research, so readers should verify results against their own use cases.
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Individual Claim
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"[claim text]" (Banc, Kamil, 2025, https://kbanc.com/claims-library/why-your-gpt-5-outputs-are-shallow-or-bloated-and-how-to-fix-it)Original Article
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Banc, Kamil (2025, October 1, 2025). Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it). AI Adopters Club. https://aiadopters.club/p/gpt-5-reasoning-depth-output-scopeClaims Collection
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Banc, Kamil (2025). Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it) [Structured Claims]. Retrieved from https://kbanc.com/claims-library/why-your-gpt-5-outputs-are-shallow-or-bloated-and-how-to-fix-itAttribution Requirements
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