Context Overflow: What Happens When Your Input Exceeds the Token Window

Context overflow occurs when the number of tokens in a prompt exceeds a model’s maximum window size. Because AI models cannot process unlimited text, they must truncate or discard excess tokens. Understanding how overflow happens—and how different models handle it—is essential for maintaining coherence and accuracy in long conversations.

When a prompt exceeds the window size, most models apply a simple truncation rule: they remove tokens from the beginning of the text until the remaining sequence fits within the limit. This can result in the loss of critical information, particularly in multi-turn conversations where earlier messages contain important context.

Some models use attention pruning or compression strategies to preserve more meaningful segments when possible. For example, certain architectures prioritize system instructions or recent dialogue over background information. However, these behaviors vary by model and are not always predictable.

Context overflow also affects model reasoning. When earlier instructions are removed, the model may shift tone, repeat questions, or generate contradictions. These effects occur because the model no longer has access to the original context that guided its earlier responses.

Developers can mitigate overflow by using summarization loops, token compression, and structured prompting. These techniques preserve meaning while reducing token usage, enabling longer interactions without sacrificing essential information.

In essence, context overflow represents the boundary of a model’s short-term memory. Knowing how it works helps users design more robust interactions and avoid unexpected loss of coherence in extended sessions.