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TEMPLATES
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Kimi K2.7 Code API: long-context coding model for repositories and agents

Use Kimi K2.7 Code online or by API for repository review, long-context coding tasks, agent traces, and multi-file technical reasoning.

InputAIReiter $0.95 per 1M tokensOutputAIReiter $4.00 per 1M tokensCache readAIReiter $0.19 per 1M tokens
Run with API
PlaygroundReadmeAPI

INPUT

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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api",
});

Run kimi-k2.7-code:

const message = await client.messages.create({
    "model": "kimi-k2.7-code",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Review this migration plan as a senior engineer and list the risky assumptions."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "kimi-k2.7-code",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Review this migration plan as a senior engineer and list the risky assumptions."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

pip install anthropic

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api",
)

Run kimi-k2.7-code:

message = client.messages.create(
      model = "kimi-k2.7-code",
      max_tokens = 2048,
      messages = [
        {
          role = "user",
          content = "Review this migration plan as a senior engineer and list the risky assumptions."
        }
      ],
      temperature = 0.7,
      top_p = 1
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "kimi-k2.7-code",
      max_tokens = 2048,
      messages = [
        {
          role = "user",
          content = "Review this migration plan as a senior engineer and list the risky assumptions."
        }
      ],
      temperature = 0.7,
      top_p = 1
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run kimi-k2.7-code against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "kimi-k2.7-code",
  "max_tokens": 2048,
  "messages": [
    {
      "role": "user",
      "content": "Review this migration plan as a senior engineer and list the risky assumptions."
    }
  ],
  "temperature": 0.7,
  "top_p": 1
}'

Add "stream": true to the body to receive the response as server-sent events.

OUTPUT

Example

A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers 429 Too Many Requests instead.

Handling 429

  1. Read the Retry-After response header. When present it tells you exactly how long to wait, in seconds.
  2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.
  3. Cap the number of retries, then surface the failure instead of looping forever.
async function withRetry(request, maxRetries = 4) {
  for (let attempt = 0; ; attempt++) {
    const response = await request();
    if (response.status !== 429 || attempt === maxRetries) return response;
    const retryAfter = Number(response.headers.get("retry-after"));
    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;
    await new Promise((resolve) => setTimeout(resolve, backoff));
  }
}

Treat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.

{
  "model": "kimi-k2.7-code",
  "input": {
    "model": "kimi-k2.7-code",
    "max_tokens": 2048,
    "messages": [
      {
        "role": "user",
        "content": "Review this migration plan as a senior engineer and list the risky assumptions."
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  },
  "output": "A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers **429 Too Many Requests** instead.\n\n## Handling 429\n\n1. Read the `Retry-After` response header. When present it tells you exactly how long to wait, in seconds.\n2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.\n3. Cap the number of retries, then surface the failure instead of looping forever.\n\n```js\nasync function withRetry(request, maxRetries = 4) {\n  for (let attempt = 0; ; attempt++) {\n    const response = await request();\n    if (response.status !== 429 || attempt === maxRetries) return response;\n    const retryAfter = Number(response.headers.get(\"retry-after\"));\n    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;\n    await new Promise((resolve) => setTimeout(resolve, backoff));\n  }\n}\n```\n\nTreat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.",
  "metrics": {
    "input_tokens": 26,
    "output_tokens": 214,
    "generated_in_seconds": 4.1
  },
  "example": true
}
Generated in
4.1 seconds
Input tokens
26
Output tokens
214
Tokens per second
52.20 tokens / second
Time to first token
-

Model details

Use the same model key in Playground, API requests, and internal workflows.

Model ID
kimi-k2.7-code
Provider
moonshot
Protocol
Anthropic Messages
Context window
262,144 tokens
Max output
-
Input tokens
95 credits / 1M tokens
Output tokens
400 credits / 1M tokens
Cache read
19 credits / 1M tokens
Cache write
-

Keep more of the codebase in the same reasoning loop

Kimi K2.7 Code is the better candidate when the hard part is not a single function, but the relationships across files, plans, and prior agent steps.

Kimi K2.7 Code API cover

Should you choose Kimi K2.7 Code?

Position it for codebase-level review, migration planning, and long agent traces where context loss is the main failure mode.

Choose it when

You need repository analysis, multi-file reasoning, long tool traces, or coding tasks where retrieval/chunking would hide important details.

Use another model when

The prompt is short, stateless, or mainly classification; a smaller model is cheaper and easier to evaluate.

Public API protocol

Call POST https://aireiter.com/api/v1/messages with model "kimi-k2.7-code". Streaming is supported through the same Messages-compatible endpoint.

Token and cache usage

Input, cache-read, and output usage should be checked after every long request. Cache reads require matching reusable prompt prefixes; repetition alone is not proof.

Kimi K2.7 Code production workloads

Best for code work where more preserved context changes the quality of the answer.
01

Repository review

Ask about architecture, dependencies, and risks across many files instead of one isolated snippet.

02

Migration planning

Keep old behavior, new requirements, and implementation notes in one request before editing.

03

Agent trace analysis

Review tool calls, failed attempts, and retained state to identify where an automated coding flow went wrong.

04

Code documentation

Summarize large modules into docs, onboarding notes, or review-ready change plans.

How Kimi K2.7 Code fits your model stack

Do not route every request to the newest model. Pick the cheapest model that still passes your quality bar, then reserve deeper models for failures or high-risk tasks.

For fast batches

Use DeepSeek V4 Flash for small coding questions; use Kimi K2.7 Code when context volume is the issue.

For deeper reasoning

Use DeepSeek V4 Pro when the prompt is compact but the reasoning risk is high.

For long context

Use Kimi K2.7 Code or MiniMax M3 when preserving context is more important than raw speed.

For production rollout

Start with one real repository task, inspect output quality, then decide whether it replaces retrieval for that workflow.

Kimi K2.7 Code API questions

Questions developers usually check before moving a text model from playground testing to production API traffic.

/ 01

What model ID should I send for Kimi K2.7 Code?

Use "kimi-k2.7-code" in the API request body. The internal DB key is only used by AIReiter routing.

/ 02

Which endpoint should Kimi K2.7 Code use?

Use POST https://aireiter.com/api/v1/messages for public API calls. Keep x-api-key / Authorization authentication consistent with your AIReiter API key setup.

/ 03

Does Kimi K2.7 Code support streaming?

Yes. Send stream=true and read server-sent events until the message completes. Test non-streaming first when debugging authentication or model ID issues.

/ 04

How do I confirm token and cache billing for Kimi K2.7 Code?

Check the usage object returned by the API. Input, output, and cache-read token fields are the source of truth for settlement; a repeated prompt alone does not prove a cache hit.

/ 05

Should I always set max_tokens for Kimi K2.7 Code?

Set max_tokens high enough for multi-file answers. Too small a limit can produce a useful diagnosis but cut off the actual migration plan.

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Questions? Contact us at
support@aireiter.com

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