Kimi cheapest long-context backend for Chinese SaaS

This pricing turns Kimi from a consumer chatbot into infrastructure for any Chinese software product that needs to keep very large documents, codebases, or past conversations in memory cheaply. Context caching matters because developers can store the expensive part of a long prompt once, then reuse it across many calls, which lowers cost and latency for apps like contract review, customer support copilots, and coding assistants built on large context windows.

• Moonshot built Kimi around long context from the start, with Kimi positioned for document heavy Chinese language workflows and supported by a disaggregated key value cache system called Mooncake that helps serve massive context windows with low delay. Cheap caching is the monetization layer on top of that technical edge.
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• The strategic buyer is a SaaS team that repeatedly sends the same large knowledge base into the model, like a law tech product reusing a case archive or an enterprise search app reusing company manuals. Paying 5 yuan per million cached tokens makes that workflow practical, because the stored context can be reused instead of recomputed on every request.
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• This also changes the competitive set. DeepSeek pushed headline token prices down to 0.1 yuan per million tokens, and Baidu cut ERNIE cache pricing sharply, but Moonshot is competing on the specific job of cheap long context serving, not just lowest raw inference price. That is closer to how Anthropic used long context and prompt caching to win API share with enterprise developers.
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The next step is a broader developer platform built around specialized Kimi models for vision, audio, coding, and formal reasoning. If Moonshot keeps long context cheap while layering higher value domain models on top, it can move from selling chat to selling the memory layer behind Chinese AI software.