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一篇读懂 Token 计费:上下文、输入输出与一份成本优化清单

「这个应用每月的模型账单会是多少」——回答这个问题需要真正理解 token 计费的口径:输入输出分价、上下文累积、缓存折扣、以及隐藏的成本大头。本篇拆解计费的完整口径、一笔真实对话的成本拆解、以及按效排序的成本优化清单——把「token 焦虑」变成「token 预算学」。
来源:原创2026-11-03阅读 1·访客 1
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Google rolls out improved SynthID AI content detector, now available globally
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SpecFold: Folding Multi-Branch Redundancy for Faster Speculative Decoding in Diffusion Language Models
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Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
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TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
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Sherpa: Teaching LLMs to Teach Adaptively
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大模型 HuggingFace Daily Papers · 10-6 阅读 14·访客 14
Anthropic 举报了与 Claude 聊天中发出威胁的佛罗里达女子]
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VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
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大模型 TechCrunch · 10-6 阅读 138·访客 138
Stepped MoE: Segment-Level Routing with Configurable Inference Complexity
Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with var…
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GPT-6 Astra vs GPT-6.1 Sol vs Gemini 4 Argon vs Claude Fable 5.1: Which Frontier Model Fits Which Job
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