How do I call Qwen3.5-9B 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/qwen3.5-9b". Nothing else in your code changes.

What does Qwen3.5-9B cost in toman?

58,857 toman per 1M input tokens and 86,555 toman per 1M output tokens; a 1,000-word request is around 189 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Qwen3.5-9B take?

Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.

Does Qwen3.5-9B 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.

Qwen3.5-9B API: toman pricing and code

qwen/qwen3.5-9b

Image inputTool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

58,857 toman

Output · per 1M tokens

86,555 toman

One 1,000-word request ≈

189 toman

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

Qwen3.5-9B 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/qwen3.5-9b",
    "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/qwen3.5-9b",
    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/qwen3.5-9b",
  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 Qwen3.5-9B good for?

Qwen3.5-9B comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3.5-9b". 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 235,929 tokens. On price it sits in the "very cheap" band — cheaper than 343 and dearer than 50 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 189 toman per 1,000-word exchange (58,857 in, 86,555 out, per 1M tokens). 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 Haiku 5.5 (batch): Qwen3.5-9B works out about 40% more expensive, 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 529 thousand-word requests on Qwen3.5-9B, and every 1,000 toman is about 5,291 words of round trip. A job with one million input tokens and one million output tokens comes to 145,412 toman in total. Filling this model's 262,144-token window costs 15,429 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.6-35b-a3b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.4× cheaper (189 against 450 toman). On parameters, this one takes max_completion_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, max_completion_tokens, min_p, 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.

What to use it for: high-volume work such as classification, tagging and bulk summarising, 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

Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...

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	123 toman
Summarising a ten-page document	4,000 in + 600 out	287 toman
Classifying a thousand short rows	120,000 in + 20,000 out	8,794 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

123 toman

Summarising a ten-page document

4,000 in + 600 out

287 toman

Classifying a thousand short rows

120,000 in + 20,000 out

8,794 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
23 Oct 2026	58,857	86,555
21 Oct 2026	27,698	45,009
7 Sep 2026	34,622	51,933

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

Frequently asked

How do I call Qwen3.5-9B 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/qwen3.5-9b". Nothing else in your code changes.
What does Qwen3.5-9B cost in toman?: 58,857 toman per 1M input tokens and 86,555 toman per 1M output tokens; a 1,000-word request is around 189 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.5-9B take?: Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3.5-9B 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.