Name: [PDG 449] Context Rot: How Increasing Input Tokens Impacts LLM Performance
Start: 2025-08-19T20:00:00+02:00
End: 2025-08-19T22:00:00+02:00

**Link to article**: https://research.trychroma.com/context-rot
**Title:** Context Rot: How Increasing Input Tokens Impacts LLM Performance
**Content**: Large Language Models (LLMs) are commonly assumed to process context uniformly, treating the 10,000th token as reliably as the 100th, but this assumption doesn't hold true in practice. A study evaluating 18 state-of-the-art LLMs, including GPT-4.1, Claude 4, Gemini 2.5, and Qwen3, found that model performance varies significantly with input length, even on simple tasks. The results show that models become increasingly unreliable as input length grows, demonstrating they do not use their context uniformly.
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[PDG 449] Context Rot: How Increasing Input Tokens Impacts LLM Performance

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[PDG 449] Context Rot: How Increasing Input Tokens Impacts LLM Performance

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