Context Rot: How increasing input tokens impacts LLM performance

I work on research at Chroma, and I just published our latest technical report on context rot. TLDR: Model performance is non-uniform across context lengths, including state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. This highlights the need for context engineering. Whether relevant information is present in a model’s context is not all that matters; what matters more is how that information is presented. Here is the complete open-source codebase to replicate our results: https://github.com/chroma-core/context-rot