Context Rot: How increasing input tokens impacts LLM performance

The article discusses the phenomenon of 'context rot' in LLMs, where performance degrades as the input context length increases. It highlights that even state-of-the-art models like GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 are affected. The research emphasizes the importance of context engineering, suggesting that how information is presented within the context is crucial. The article provides an open-source codebase for replicating the results.

• •LLM performance degrades with increasing context length (context rot).
• •Even state-of-the-art models are affected.
• •Context engineering is crucial for optimal performance.
• •Open-source codebase available for replication.