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
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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% similarUnverified随着大模型推理能力增强,单次调用消耗的token显著攀升,在固定用量限制下形成「更强模型」与「更快触及上限」的悖论75% similarUnverifiedReasoning Effort档位越高,模型可消耗的思考Token上限越大,但延迟和Token成本相应线性乃至超线性增长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
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