Kimi K2 0905 API: toman pricing and code
moonshotai/kimi-k2-0905
toolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Kimi K2 0905 example: tool calling
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
tools = [{
"type": "function",
"function": {
"name": "check_stock",
"description": "Returns the stock level of a product",
"parameters": {
"type": "object",
"properties": {"sku": {"type": "string"}},
"required": ["sku"],
},
},
}]
messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="moonshotai/kimi-k2-0905", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]
# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}
messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="moonshotai/kimi-k2-0905", messages=messages, tools=tools)
print(final.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const tools = [{
type: "function",
function: {
name: "check_stock",
description: "Returns the stock level of a product",
parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
},
}];
const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "moonshotai/kimi-k2-0905", messages, tools });
const call = first.choices[0].message.tool_calls[0];
const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };
messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "moonshotai/kimi-k2-0905", messages, tools });
console.log(final.choices[0].message.content);curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/kimi-k2-0905",
"messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
"tools": [{
"type": "function",
"function": {
"name": "check_stock",
"parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
}
}]
}'What is Kimi K2 0905 good for?
Kimi K2 0905 comes from Moonshot (Kimi); in uttapen you reach it with the model id "moonshotai/kimi-k2-0905". 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 100,352 tokens. On price it sits in the "mid-range" band — cheaper than 228 and dearer than 178 of the other paid models in the catalogue.
What you get on top of text in, text out: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
Input runs at 174,075 toman per 1M tokens and output at 725,313 — output costs 4.2× input, so trimming the answer saves more than trimming the prompt. 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 R1: Kimi K2 0905 works out roughly 1× 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 86 thousand-word requests on Kimi K2 0905, and every 1,000 toman is about 855 words of round trip. A job with one million input tokens and one million output tokens comes to 899,388 toman in total. Filling this model's 262,144-token window costs 45,633 toman on the input side alone, which is the real reason to keep conversation history short.
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 repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32...
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 | 551 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,131 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 35,395 toman |
Frequently asked
- How do I call Kimi K2 0905 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-k2-0905". Nothing else in your code changes.
- What does Kimi K2 0905 cost in toman?
- 174,075 toman per 1M input tokens and 725,313 toman per 1M output tokens; a 1,000-word request is around 1,169 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Kimi K2 0905 take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 100,352 tokens.
- Does Kimi K2 0905 support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.