Unverified50% confidenceTradeoffExact time
In scenarios requiring deep reasoning or multi-turn long-context understanding, locally running small language models generally produce lower-quality answers than cloud-based large models.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
Verified大语言模型推理的瓶颈往往不在计算能力，而在于内存带宽79% similarVerifiedLarge language models exhibit emergent capabilities, suddenly displaying abilities not present at smaller scales once the model size exceeds a certain threshold.78% similarUnverifiedLarge language models show preliminary capabilities on certain Theory of Mind tasks, but still have significant limitations in complex multi-turn, multi-party scenarios.76% similarUnverifiedLarge language models have systematic shortcomings in 2D spatial reasoning, due to training data being predominantly text-based and lacking supervisory signals for spatial relationships.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/996402API
curl https://kongchang.com/api/v1/knowledge/claims/996402MCP
get_claim(id=996402)