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Qwen3 VL 30B A3B Instruct API: toman pricing and code

qwen/qwen3-vl-30b-a3b-instruct

Image inputTool callingJSON output
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
207,732 toman
One 1,000-word request ≈
338 toman

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

Qwen3 VL 30B A3B Instruct 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-vl-30b-a3b-instruct",
    "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-vl-30b-a3b-instruct",
    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-vl-30b-a3b-instruct",
  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 VL 30B A3B Instruct good for?

Qwen3 VL 30B A3B Instruct comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3-vl-30b-a3b-instruct". 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 16,384 tokens. On price it sits in the "cheap" band — cheaper than 282 and dearer than 103 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Input runs at 51,933 toman per 1M tokens and output at 207,732 — output costs 4× 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 MiniMax M3.1 Flash, and Qwen3 VL 30B A3B Instruct 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 296 thousand-word requests on Qwen3 VL 30B A3B Instruct, and every 1,000 toman is about 2,959 words of round trip. A job with one million input tokens and one million output tokens comes to 259,665 toman in total. Filling this model's 262,144-token window costs 13,614 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-vl-8b-instruct", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 23% cheaper (338 against 450 toman). Maximum answer length differs as well: 16,384 against 235,929 tokens. On parameters, this one takes min_p.

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. 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 Aion 3.5 at 262,144 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, 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-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...

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 out161 toman
Summarising a ten-page document4,000 in + 600 out332 toman
Classifying a thousand short rows120,000 in + 20,000 out10,387 toman

Price history

DateInput (toman/1M)Output (toman/1M)
25 Sep 202651,933207,732
20 Sep 202645,009180,034
20 Sep 202669,244242,354
19 Sep 202645,009180,034
19 Sep 202669,244242,354
19 Sep 202645,009180,034
19 Sep 202669,244242,354
16 Sep 202645,009180,034
16 Sep 202651,933207,732
16 Sep 202645,009180,034
7 Sep 202651,933207,732

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

Frequently asked

How do I call Qwen3 VL 30B A3B Instruct 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-vl-30b-a3b-instruct". Nothing else in your code changes.
What does Qwen3 VL 30B A3B Instruct cost in toman?
51,933 toman per 1M input tokens and 207,732 toman per 1M output tokens; a 1,000-word request is around 338 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 VL 30B A3B Instruct take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 16,384 tokens.
Does Qwen3 VL 30B A3B Instruct 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.