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Qwen3.6 27B API: toman pricing and code

qwen/qwen3.6-27b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
155,799 toman
Output · per 1M tokens
934,792 toman
One 1,000-word request ≈
1,418 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Qwen3.6 27B 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": "qwen/qwen3.6-27b",
    "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="qwen/qwen3.6-27b",
    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: "qwen/qwen3.6-27b",
  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 Qwen3.6 27B good for?

Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3.6-27b". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 156 and dearer than 241 of the other paid models in the catalogue.

What it can do beyond plain text: 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.

Pricing is 155,799 toman per 1M input tokens and 934,792 per 1M output tokens. A 1,000-word round trip on Qwen3.6 27B lands near 1,418 toman. 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 Step 5 Preview: Qwen3.6 27B works out about 17% cheaper, 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 71 thousand-word requests on Qwen3.6 27B, and every 1,000 toman is about 705 words of round trip. A job with one million input tokens and one million output tokens comes to 1,090,591 toman in total. Filling this model's 262,144-token window costs 40,842 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.5-27b", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,418 and 1,287 toman). Maximum answer length differs as well: 65,536 against 81,920 tokens.

Qwen (Alibaba) has 42 models in our catalogue; the cheapest is Qwen3.7 Flash at 72 toman per thousand words and the dearest Qwen3.8 Max Prime at 7,201. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Good fits: 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

Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs...

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 out608 toman
Summarising a ten-page document4,000 in + 600 out1,184 toman
Classifying a thousand short rows120,000 in + 20,000 out37,392 toman

Price history

DateInput (toman/1M)Output (toman/1M)
31 Oct 2026155,799934,792
31 Oct 2026110,7901,107,902
28 Oct 2026155,799934,792
25 Oct 2026110,7901,107,902
15 Oct 202686,555692,439
15 Oct 2026110,7901,107,902
15 Oct 2026155,799934,792
9 Oct 2026110,7901,107,902
9 Oct 2026155,799934,792
9 Oct 2026110,7901,107,902
9 Oct 2026155,799934,792
8 Oct 2026103,866692,439

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

Frequently asked

How do I call Qwen3.6 27B 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 "qwen/qwen3.6-27b". Nothing else in your code changes.
What does Qwen3.6 27B cost in toman?
155,799 toman per 1M input tokens and 934,792 toman per 1M output tokens; a 1,000-word request is around 1,418 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.6 27B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
Does Qwen3.6 27B 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.