Unverified50% confidenceFactExact time
模型规模（参数量）与推理成本之间存在超线性关系，参数量翻倍会导致推理延迟与算力开销增长远超两倍
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
GPT 5.6+Codex深度实测：三档模型与Ultra模式全解析
bilibilioil欧呦7/10/2026
Related Claims
Unverified推理预算越高，模型响应延迟和 Token 消耗成倍增加，但在数学证明、复杂算法设计等场景表现显著提升78% similarUnverifiedAs large model reasoning capabilities strengthen, the tokens consumed per single call rise significantly, creating a paradox of 'stronger model' versus 'faster limit reached' under fixed usage limits.75% similarUnverified推理时计算的扩展虽能提升单次输出质量，但成本以超线性方式增长，大规模部署往往难以经济可行73% similarUnverified每次推理都需重新处理全部上下文，计算成本随上下文长度呈二次方增长73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/501743API
curl https://kongchang.com/api/v1/knowledge/claims/501743MCP
get_claim(id=501743)