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Kimi K3 API: toman pricing and code

moonshotai/kimi-k3

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
66,474 toman
Output · per 1M tokens
5,539,510 toman
One 1,000-word request ≈
7,288 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 63,012 toman per 1M tokens.Pricing and top-ups

Kimi K3 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-k3",
    "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-k3",
    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-k3",
  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 K3 good for?

The id for Kimi K3 in our API is "moonshotai/kimi-k3", served from Moonshot (Kimi). Context is 1,048,576 tokens; past that you have to summarise the history yourself. A single response can run to 943,718 tokens. On price it sits in the "expensive" band — cheaper than 30 and dearer than 367 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 66,474 toman per 1M tokens and output at 5,539,510 — output costs 83.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 63,012 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 Claude Sonnet 4.5: Kimi K3 works out about 9% cheaper, and its context window is larger. 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 14 thousand-word requests on Kimi K3, and every 1,000 toman is about 137 words of round trip. A job with one million input tokens and one million output tokens comes to 5,605,984 toman in total. Filling this model's 1,048,576-token window costs 69,703 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "moonshotai/kimi-k3:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out about 20% more expensive (7,288 against 6,157 toman). Maximum answer length differs as well: 943,718 against 16,384 tokens. On parameters, this one takes logprobs, max_completion_tokens, parallel_tool_calls, top_logprobs.

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, max_completion_tokens, min_p, parallel_tool_calls, presence_penalty — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Where it makes sense: work where the quality of the answer matters more than its cost, 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

Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...

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 out2,316 toman
Summarising a ten-page document4,000 in + 600 out3,590 toman
Classifying a thousand short rows120,000 in + 20,000 out118,767 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
batchmoonshotai/kimi-k3:batch3,946,9011,048,576

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202666,4745,539,510
5 Nov 202669,2445,539,510
4 Nov 202666,4745,539,510
31 Oct 202666,4745,193,290
29 Oct 202682,7463,739,169
29 Oct 202666,4745,193,290
28 Oct 202666,4746,924,387
28 Oct 202669,5906,924,387
28 Oct 202676,1686,924,387
28 Oct 202679,9776,924,387
28 Oct 202660,2426,924,387
28 Oct 202652,6256,924,387

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

Frequently asked

How do I call Kimi K3 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-k3". Nothing else in your code changes.
What does Kimi K3 cost in toman?
66,474 toman per 1M input tokens and 5,539,510 toman per 1M output tokens; a 1,000-word request is around 7,288 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Kimi K3 take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does Kimi K3 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.