提示缓存:跨供应商的成本与性能分析
提示缓存可将输入 token 成本降低多达 90%,并使长上下文工作负载变得可行。在下方比较各大 AI 供应商的缓存价格、折扣和 API 规格。
缓存要求提示词完全匹配,且因供应商而异:OpenAI 和 DeepSeek 等提供自动缓存,而 Google、Anthropic 和 Amazon 等则需要手动设置。了解其工作原理,请参阅下方的提示缓存介绍。
价格
价格:缓存命中、缓存写入、输入和输出
Price (USD per M Tokens)
缓存折扣
Pricing: Cache Discount
1 - (cache hit price / input price) · Higher is better
提示缓存 API 规格
供应商 | 模型 | 输入(标准) | 缓存写入 | 缓存命中 | 缓存存储 | 输出(标准) | 自动启用 | 最小 token 数 | 缓存 TTL | 备注 |
|---|---|---|---|---|---|---|---|---|---|---|
Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) | $10.00 | $12.50 | $0.25 | $20.00 | $50.00 | - | - | |||
Claude Opus 5 (Adaptive Reasoning, Max Effort) | $5.00 | $6.25 | $0.50 | $10.00 | $25.00 | - | - | 1h cache write: $10 | ||
Claude Opus 5 (Adaptive Reasoning, Max Effort) | $5.00 | $6.25 | $0.50 | - | $25.00 | - | - | |||
GPT-5.6 Sol (max) | $5.50 | $6.88 | $0.55 | - | $33.00 | - | - | |||
Claude Opus 5 (Adaptive Reasoning, Max Effort) | $5.00 | $6.25 | $0.50 | - | $25.00 | - | - | |||
Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) | $10.00 | $12.50 | $1.00 | - | $50.00 | - | - | |||
GPT-5.6 Sol (max) | $4.00 | $5.00 | $0.40 | - | $20.00 | - | - | |||
GPT-5.6 Terra (max) | $2.00 | $2.50 | $0.20 | - | $12.00 | - | - | |||
Grok 4.6 (high) | $2.00 | - | $0.50 | - | $6.00 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
Kimi K3 (max) | $2.66 | - | $0.28 | - | $13.30 | - | - | |||
Qwen3.8 2.4T A95B | $1.90 | - | $0.19 | - | $5.70 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
GLM-5.3 (max) | $1.40 | - | $0.26 | - | $4.40 | - | - | |||
Kimi K3 (max) | $2.85 | - | $0.28 | - | $14.25 | - | - | |||
GLM-5.3 (max) | $1.40 | - | $0.26 | - | $4.40 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
Kimi K3 (max) | $4.50 | - | $0.45 | - | $22.50 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
Kimi K3 (max) | $2.55 | - | $0.26 | - | $12.75 | - | - | |||
GLM-5.3 (max) | $1.35 | - | $0.23 | - | $4.40 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | - | ||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
Kimi K3 (max) | $3.00 | - | $0.30 | - | $15.00 | - | - | |||
Qwen3.8 2.4T A95B | $2.50 | - | $0.50 | - | $6.25 | - | - | |||
GLM-5.3 (max) | $1.20 | - | $0.12 | - | $4.00 | - | - | |||
Qwen3.8 2.4T A95B | $2.00 | - | $0.20 | - | $6.00 | - | - | |||
GLM-5.3 (max) | $1.40 | - | $0.26 | - | $4.40 | - | - | |||
MiniMax-M3 | $0.30 | - | $0.06 | - | $1.20 | - | - | |||
GLM-5.3 (max) | $1.40 | - | $0.26 | - | $4.40 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
| Qwen3.8 2.4T A95B | $2.00 | - | $0.25 | - | $6.00 | - | - | ||
Qwen3.8 27B (xhigh) | $0.50 | - | $0.05 | - | $3.00 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
GLM-5.3-Flash | $0.07 | - | $0.01 | - | $0.25 | - | - | |||
DeepSeek V4 Pro 0813 (Reasoning, Max Effort) | $1.12 | - | $0.04 | - | $3.37 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
DeepSeek V4 Pro 0813 (Reasoning, Max Effort) | $1.32 | - | $0.13 | - | $3.96 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
DeepSeek V4 Pro 0813 (Reasoning, Max Effort) | $1.50 | - | $0.14 | - | $3.13 | - | - | |||
GLM-5.3-Flash | $0.15 | - | $0.03 | - | $0.50 | - | - | |||
Muse Spark 1.2 (xhigh) | $1.25 | - | $0.15 | - | $4.25 | - | - | - | ||
DeepSeek V4 Pro 0813 (Reasoning, Max Effort) | $1.32 | - | $0.04 | - | $3.96 | - | - | |||
Qwen3.8 27B (xhigh) | $0.40 | - | $0.15 | - | $3.00 | - | - | |||
MiniMax-M3 | $0.23 | - | $0.05 | - | $0.96 | - | - | |||
Mistral Medium 3.5 | $1.50 | - | $0.15 | - | $7.50 | - | - | |||
MiniMax-M3 | $0.30 | $0.38 | $0.06 | - | $1.20 | - | - | |||
Inkling (xhigh) | $1.00 | - | $0.17 | - | $4.05 | - | - | - | ||
gpt-oss-120b (high) | $0.15 | - | $0.07 | - | $0.60 | - | 2 hrs |
提示缓存介绍
什么是提示缓存?
提示缓存让语言模型推理可以复用已经处理过的输入 token,将其成本降低多达 90%,并使长上下文工作负载变得可行。把缓存策略做对,可以在输入 token 上带来巨大节省,并带来有意义的性能提升。
当你发送提示词时,系统会先检查该精确提示词是否已经处理过。如果找到(缓存命中),就会返回已存储的响应,而不是重新生成。如果未找到(缓存未命中),提示词会正常处理,并将响应存起来供以后使用。
需要关注的关键指标
- 输入价格:你为输入 token 支付的标准价格
- 缓存写入价格:将提示 token 写入缓存所需支付的费用;有时高于标准输入价格
- 缓存命中价格:命中缓存的提示 token 的折扣费率
- 缓存存储价格:每百万已缓存 token 的每小时成本(目前为 Google 独有)
- 缓存 TTL:已缓存 token 保持可用的时间,从数小时到数天不等
- 缓存最小 token 数:在提供缓存命中之前所需的最小匹配 token 数量
提示缓存如何工作?
当你向基于 transformer 的语言模型发送提示词时,注意力层会将每个输入 token 处理成键(K)和值(V)向量,并存储在 KV 缓存中。将这些值保留在内存中后,当再次向模型发送相同的输入 token 时,就可以避免重复处理。
直到最近,利用缓存带来的速度和成本优势还仅限于专用部署。现在,serverless API 供应商——包括前沿实验室——已经开始把部分缓存成本优势转给开发者。
最佳使用场景
- 系统指令:必须在多次交互中包含的大型系统提示词
- 聊天历史:伴随用户每一轮新输入的对话上下文
- 按用户个性化的上下文:用于深度个性化的大量用户记忆或资料
实现注意事项
- 激活方式因供应商而异:有的需要手动设置,有的提供自动缓存
- 缓存命中折扣相当于标准输入 token 价格的 50–90% 优惠——值得花时间把它做对
- 缓存可以提升超长提示词(5 万+ token)的性能