Moonshot API is the interface to the Kimi model family, known for long context, Chinese understanding, and cost-effectiveness. It is commonly used for long-document reading, knowledge bases, customer service, and content generation.

This guide covers how to obtain access, call the API, and avoid the integration problems that appear when long-context requests meet production traffic.

Note: Moonshot and Kimi are trademarks of their owners. This article reflects common versions as of October 2026; model names and prices follow the model pricing page.

1. What Moonshot API Is Good For

  • Long-document summaries and contract or report reading.
  • Chinese question answering and customer service.
  • Knowledge bases and RAG.
  • Content creation and rewriting.
  • Code explanation.

The long-context models are the main attraction, but they are not always the right choice. A short support reply does not need a very large context window, and using one anyway raises latency and cost without improving the answer.

2. Getting a Key

Official Moonshot

Register on the Moonshot open platform, create a key, and top up your account.

ZSQH AI Gateway

  1. Register for the ZSQH AI Gateway.
  2. Create a key and set a quota.
  3. Top up the account.
  4. Call the API with the OpenAI SDK.

A gateway is useful when the same application also uses other model families. One key and one Base URL can cover several providers, which simplifies billing and key rotation. Set a quota per key from the beginning; it is much easier to raise a limit than to explain an unexpected bill.

3. Base URL

The official Moonshot endpoint is usually:

https://api.moonshot.cn/v1

The gateway endpoint is:

https://ai.zhongshangqihang.com/v1

Use the Base URL that matches the key. A key issued by one platform will not authenticate against the other, and this mismatch is one of the most common causes of a 401 error.

4. Python Call

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
    base_url="https://ai.zhongshangqihang.com/v1",
)

resp = client.chat.completions.create(
    model="moonshot-v1-8k",
    messages=[{"role": "user", "content": "Summarize this text in three sentences: ..."}],
)
print(resp.choices[0].message.content)

Model names follow the pricing page, and long-context models usually cost more per token. Confirm the exact model name before deployment; an outdated name can return a 404 even when the key and Base URL are correct. Keep the endpoint and model name in environment configuration so that switching between a test and production account does not require code changes.

5. Working with Long Text

  1. Split or summarize first; do not fill the entire context window at once.
  2. Keep citations to the source so answers can be checked.
  3. Long-context requests are slower and more expensive, so set timeouts.
  4. Use JSON mode or tool calling for structured output when the model supports it.
  5. Verify important content with a second pass.

A useful pattern is a two-stage pipeline: first summarize each section, then ask the model to answer using the summaries plus a few relevant original passages. This keeps the prompt smaller and makes it easier to trace which part of the document supported the answer. Record the token counts returned by the API for each stage; they show whether the summary step is actually reducing the final prompt or merely adding another call.

6. Common Errors

Error Cause Action
401 Key or endpoint mismatch Check the key and Base URL
404 Wrong model name or path Check the pricing page and confirm /v1
429 Rate limit Reduce concurrency and retry
Context limit exceeded Input too long Split or summarize
Insufficient balance No credit Top up

7. Cost Control

  • Long context is the main cost driver, so summarize before asking.
  • Use a short-context model for simple tasks.
  • Process batch jobs asynchronously.
  • Monitor retries and failed requests.

Measure the input-token distribution of real traffic. A few very large requests can dominate the monthly bill, and those are often the easiest ones to optimize by trimming boilerplate or caching a document summary. Cache summaries for documents that are read repeatedly, and reuse them across users when the source and permissions are the same.

FAQ

Is the Moonshot API the same as the Kimi web app?

No. The web app is a product, while the API is an interface for programs. Billing and limits differ.

Does it support the OpenAI SDK?

It usually supports OpenAI-compatible Chat Completions. Check the official or gateway documentation for the exact parameters.

Is access stable from mainland China?

Moonshot is a domestic platform, so it is generally more stable than overseas models. Results still depend on your network and region.

Next Steps

If you need a unified entry point or multi-model switching, you can register for the ZSQH AI Gateway and test a long-document workflow with a small budget. Model names and prices are listed on the model pricing page.

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