How to Use the Kimi K3 API for Batch Document Work
Chatting with Kimi K3 one document at a time works until you have two hundred of them. At that point you want the API, where you can script the same prompt across a folder and get structured output back.
This is the practical setup: authentication, the request shape, cost control, and the failure modes that show up only at scale.
How do you authenticate?
Generate an API key from the Moonshot AI console and store it as an environment variable. Never hard-code it into a script you might commit. Calls go to the standard chat-completions endpoint, which means any OpenAI-compatible client library can talk to it with a changed base URL.
That compatibility is the main reason teams pick Kimi K3 for pipelines. Existing code often needs a two-line change rather than a rewrite.
How do you structure a batch job?
Treat each document as an independent request and store results keyed by filename. That way a failed document costs you one retry, not a rerun of the whole batch.
- Pre-process documents into plain text first. Send clean text, not raw PDFs, so you can measure token count.
- Chunk by section if a file exceeds roughly a third of the context window.
- Send one request per chunk with a fixed system prompt asking for JSON output.
- Validate the JSON before writing to your database; reject and retry anything malformed.
- Log token usage per call — you will want it when the bill arrives.
Ask for a specific JSON shape in the system prompt, with field names and types. Free-form prose output is impossible to aggregate across five hundred documents.
How do you control cost?
Cost scales with input tokens, and long documents are mostly input. Two levers: trim the text before sending, and cache the parts that repeat.
Most boilerplate — headers, footers, legal disclaimers — carries no signal. Strip it before the request. If many documents share a long instruction block, prompt caching can cut the effective price of that shared prefix, so keep invariable instructions first and variable content last.
A realistic figure: a 50,000-token document processed with a short instruction costs far less than the same document run three times because the prompt changed. Lock your system prompt early.
What breaks at scale?
Three things, consistently.
- Rate limits — add exponential backoff, not a fixed sleep. Fixed delays either waste time or still trip the limit.
- Truncation — an over-long input often gets silently cut rather than rejected. Check that responses reference your final paragraphs before trusting them.
- JSON drift — the model occasionally wraps output in markdown fences. Strip fences before parsing or your pipeline stalls on document number 40.
Should you use the API or the app?
Use the app for one-off questions and exploratory reading. Switch to the API the moment the same task repeats more than a few times. If your volume is under 20 documents a week, the app is cheaper in your time even if the tokens are free.
Quick Summary
- Kimi K3’s API is OpenAI-compatible — often a base-URL change.
- Send clean text and one request per chunk, keyed by filename.
- Demand JSON output and validate it before storing.
- Cache shared instructions; strip boilerplate to cut input tokens.
- Add exponential backoff and strip markdown fences before parsing.
