Is GPT 5’s description of its context window accurate?

Understanding the Scope and Prioritization of Context Windows in GPT-5: A Closer Examination

Artificial Intelligence (AI) models, particularly large language models like GPT-5, have revolutionized how we interact with digital content. As these models advance, so does the importance of understanding their internal mechanisms—especially how they handle the vast influx of information during interactions. One question that has garnered attention is: How accurately does GPT-5 describe its own context window and the prioritization of various inputs?

Exploring GPT-5’s Claimed Context Management Strategy

Recently, a user shared an inquiry into GPT-5’s explanation of its context window management. The user aimed to clarify the hierarchy of information sources that GPT-5 utilizes during processing tasks. The sources in question include:

• Global instructions
• Current prompts
• Project instructions
• Uploaded files and project files
• Earlier conversation messages

The user generated a visual summary (attached as a slide) based on GPT-5’s own description, which posits a specific prioritization scheme:

1. Global Instructions: These are always active and are said not to consume space within the model’s context window. Their importance is marked as very high, and they are assumed to be omnipresent regardless of active interactions.

2. Current Prompt: Holding the highest importance, the prompt directly influences the model’s response and occupies the primary part of the context window due to its immediacy.

3. Project Instructions: These are secondary to the prompt but still crucial, and they are considered to be included within the context window.

4. Project Files and Uploaded Materials: These are re-verified or reloaded dynamically to maintain high relevance within the context, though they might be subject to size constraints.

5. Earlier Messages and Files: Over time, these are considered of the lowest importance, gradually fading out of the model’s working memory as newer information takes precedence.

Evaluating the Accuracy and Completeness

While this simplified hierarchy provides a helpful conceptual framework, it’s important to recognize its limitations:

• Static vs. Dynamic Loading: The description implies a static prioritization, but in reality, GPT models dynamically manage context. Certain information may be reloaded or prioritized as needed, especially with frameworks supporting document embedding retrieval systems.

• Global Instructions: The claim that global instructions do not occupy space may oversimplify how the model incorporates instructions. They are often embedded