How do I call Qwen3.5-27B 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/qwen3.5-27b". Nothing else in your code changes.

What does Qwen3.5-27B cost in toman?

90,017 toman per 1M input tokens and 900,170 toman per 1M output tokens; a 1,000-word request is around 1,287 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Qwen3.5-27B take?

Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 81,920 tokens.

Does Qwen3.5-27B 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.

Qwen3.5-27B API: toman pricing and code

qwen/qwen3.5-27b

Image inputTool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

90,017 toman

Output · per 1M tokens

900,170 toman

One 1,000-word request ≈

1,287 toman

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

Qwen3.5-27B 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.

curlPythonNode.js

# 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/qwen3.5-27b",
    "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/qwen3.5-27b",
    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/qwen3.5-27b",
  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 Qwen3.5-27B good for?

The id for Qwen3.5-27B in our API is "qwen/qwen3.5-27b", served from Qwen (Alibaba). Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 81,920 tokens. On price it sits in the "mid-range" band — cheaper than 158 and dearer than 238 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 90,017 toman per 1M tokens and output at 900,170 — output costs 10× 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 Nova 2 Lite, and Qwen3.5-27B costs about the same, 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 78 thousand-word requests on Qwen3.5-27B, and every 1,000 toman is about 777 words of round trip. A job with one million input tokens and one million output tokens comes to 990,187 toman in total. Filling this model's 262,144-token window costs 23,597 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.6-27b", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,287 and 1,418 toman). Maximum answer length differs as well: 81,920 against 65,536 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.

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

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...

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	495 toman
Summarising a ten-page document	4,000 in + 600 out	900 toman
Classifying a thousand short rows	120,000 in + 20,000 out	28,805 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

495 toman

Summarising a ten-page document

4,000 in + 600 out

900 toman

Classifying a thousand short rows

120,000 in + 20,000 out

28,805 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
27 Oct 2026	90,017	900,170
19 Oct 2026	93,479	747,834
19 Oct 2026	67,513	540,102
8 Oct 2026	90,017	900,170
7 Sep 2026	67,513	540,102

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

Frequently asked

How do I call Qwen3.5-27B 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/qwen3.5-27b". Nothing else in your code changes.
What does Qwen3.5-27B cost in toman?: 90,017 toman per 1M input tokens and 900,170 toman per 1M output tokens; a 1,000-word request is around 1,287 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.5-27B take?: Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 81,920 tokens.
Does Qwen3.5-27B 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.