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Kimi K2.6 API: toman pricing and code

moonshotai/kimi-k2.6

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
225,043 toman
Output · per 1M tokens
1,180,608 toman
One 1,000-word request ≈
1,827 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 K2.6 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-k2.6",
    "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-k2.6",
    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-k2.6",
  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 K2.6 good for?

Kimi K2.6 is one of Moonshot (Kimi)'s models. Put "moonshotai/kimi-k2.6" in the model field and the rest of your code stays as it is. Kimi K2.6 keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 235,929 tokens. On price it sits in the "mid-range" band — cheaper than 143 and dearer than 255 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 225,043 toman per 1M tokens and output at 1,180,608 — output costs 5.2× input, so trimming the answer saves more than trimming the prompt. 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 K2.6 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 55 thousand-word requests on Kimi K2.6, and every 1,000 toman is about 547 words of round trip. A job with one million input tokens and one million output tokens comes to 1,405,651 toman in total. Filling this model's 262,144-token window costs 58,994 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-k2.7-code", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,827 and 1,845 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.

Where it makes sense: 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

Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...

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 out810 toman
Summarising a ten-page document4,000 in + 600 out1,609 toman
Classifying a thousand short rows120,000 in + 20,000 out50,617 toman

Price history

DateInput (toman/1M)Output (toman/1M)
11 Oct 2026225,0431,180,608
10 Oct 2026150,398848,237
10 Oct 2026150,536848,237
10 Oct 2026151,471848,237
10 Oct 2026152,683848,237
10 Oct 2026155,279848,237
10 Oct 2026160,992848,237
9 Oct 2026150,398848,237
9 Oct 2026150,536848,237
9 Oct 2026150,848848,237
9 Oct 2026151,471848,237
9 Oct 2026160,992848,237

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

Frequently asked

How do I call Kimi K2.6 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-k2.6". Nothing else in your code changes.
What does Kimi K2.6 cost in toman?
225,043 toman per 1M input tokens and 1,180,608 toman per 1M output tokens; a 1,000-word request is around 1,827 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Kimi K2.6 take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Kimi K2.6 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.