Kimi K2.7 Code API: toman pricing and code
moonshotai/kimi-k2.7-code
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 52,223 toman / 1M.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 255 and dearer than 151 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 1,531 toman per 1,000-word exchange (191,483 in, 986,425 out, per 1M tokens). It supports cached input: repeated context is billed at 52,223 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 Nova Pro 1.0: Kimi K2.7 Code works out roughly 1× more expensive, and its context window is smaller. 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 65 thousand-word requests on Kimi K2.7 Code, and every 1,000 toman is about 653 words of round trip. A job with one million input tokens and one million output tokens comes to 1,177,908 toman in total. Filling this model's 262,144-token window costs 50,196 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. On a thousand-word request this variant works out 1.2× cheaper (1,531 against 1,867 toman).
Moonshot (Kimi) has 9 models in our catalogue; the cheapest is Kimi K2.5 at 1,018 toman per thousand words and the dearest Kimi K3 (batch) at 6,789. Among the less common parameters it accepts logit_bias, logprobs, min_p, parallel_tool_calls, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking 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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 682 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,358 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 42,706 toman |
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?
- 191,483 toman per 1M input tokens and 986,425 toman per 1M output tokens; a 1,000-word request is around 1,531 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.