Verified75% confidenceTradeoffExact time
Expanding the context window brings quadratic growth in computational cost, and ultra-long contexts suffer from the 'lost in the middle' problem.
3
Sources
75%
Confidence
Long-term
Relevance
9/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe longer the context, the more VRAM and computational overhead grow approximately quadratically with length.79% similarVerifiedThe KV cache VRAM usage of ultra-long context windows grows linearly with context length, causing inference latency and costs to rise significantly.78% similarUnverifiedThe fuller the context window, the more the model tends to forget earlier information, while inference latency and API call costs grow linearly with token count.75% similarUnverifiedThe computational cost of ultra-long context models grows non-linearly with context length.74% similar
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