Unverified50% confidenceFactExact time
The 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
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Related Claims
UnverifiedNative large models are constrained by context window size and tend to forget early information when handling ultra-long tasks.76% similarUnverifiedEach LLM call for page understanding introduces latency and API call cost issues, and the cumulative cost of multi-step tasks may exceed user expectations.76% similarVerifiedThe KV cache VRAM usage of ultra-long context windows grows linearly with context length, causing inference latency and costs to rise significantly.76% similarUnverified在推理阶段输入内容通过并行化注意力机制一次性处理，而输出内容需自回归逐Token生成，无法并行化，构成大模型推理延迟的根本瓶颈75% similar
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