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Kimi K2.7 Code API: toman pricing and code

moonshotai/kimi-k2.7-code

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
207,732 toman
Output · per 1M tokens
1,211,768 toman
One 1,000-word request ≈
1,845 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 51,933 toman per 1M tokens.Pricing and top-ups

Kimi K2.7 Code example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2.7-code",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="moonshotai/kimi-k2.7-code",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "moonshotai/kimi-k2.7-code",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Kimi K2.7 Code good for?

Kimi K2.7 Code comes from Moonshot (Kimi); in uttapen you reach it with the model id "moonshotai/kimi-k2.7-code". It accepts up to 262,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 235,929 tokens. On price it sits in the "mid-range" band — cheaper than 141 and dearer than 256 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

For a back-of-envelope figure: about 1,845 toman per 1,000-word exchange (207,732 in, 1,211,768 out, per 1M tokens). It supports cached input: repeated context is billed at 51,933 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Qwen3.5 397B A17B, and Kimi K2.7 Code costs about the same, and its context window is the same size. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 54 thousand-word requests on Kimi K2.7 Code, and every 1,000 toman is about 542 words of round trip. A job with one million input tokens and one million output tokens comes to 1,419,499 toman in total. Filling this model's 262,144-token window costs 54,456 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "moonshotai/kimi-k2.6", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,845 and 1,827 toman).

Moonshot (Kimi) has 10 models in our catalogue; the cheapest is Kimi K2 0711 at 1,292 toman per thousand words and the dearest Kimi K3 at 7,288. Among the less common parameters it accepts logit_bias, logprobs, min_p, parallel_tool_calls, presence_penalty, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out796 toman
Summarising a ten-page document4,000 in + 600 out1,558 toman
Classifying a thousand short rows120,000 in + 20,000 out49,163 toman

Price history

DateInput (toman/1M)Output (toman/1M)
31 Oct 2026207,7321,211,768
28 Oct 2026193,8831,073,280
28 Oct 2026232,8331,073,280
28 Oct 2026193,8831,073,280
28 Oct 2026232,8331,073,280
27 Oct 2026193,8831,073,280
27 Oct 2026232,8331,073,280
27 Oct 2026193,8831,073,280
27 Oct 2026232,8331,073,280
27 Oct 2026193,8831,073,280
26 Oct 2026232,3821,159,835
26 Oct 2026232,8331,073,280

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Kimi K2.7 Code from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "moonshotai/kimi-k2.7-code". Nothing else in your code changes.
What does Kimi K2.7 Code cost in toman?
207,732 toman per 1M input tokens and 1,211,768 toman per 1M output tokens; a 1,000-word request is around 1,845 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Kimi K2.7 Code take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Kimi K2.7 Code support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.