Qwen3 VL 32B Instruct API: toman pricing and code
qwen/qwen3-vl-32b-instruct
visiontoolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Qwen3 VL 32B 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-32b-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-32b-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-32b-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 32B Instruct good for?
Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3-vl-32b-instruct". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 32,768 tokens. On price it sits in the "very cheap" band — cheaper than 83 and dearer than 323 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 30,173 toman per 1M tokens and output at 120,692 — 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 Seed-2.0-Mini: Qwen3 VL 32B Instruct works out roughly 1× 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 510 thousand-word requests on Qwen3 VL 32B Instruct, and every 1,000 toman is about 5,102 words of round trip. A job with one million input tokens and one million output tokens comes to 150,865 toman in total. Filling this model's 131,072-token window costs 3,955 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. Both land at nearly the same price on a thousand-word request (196 and 216 toman). The context windows differ too: 131,072 against 262,144 tokens. On parameters the other takes logit_bias, repetition_penalty.
Qwen (Alibaba) has 53 models in our catalogue; the cheapest is Qwen3.7 Flash at 60 toman per thousand words and the dearest Qwen3.8 Max (0902) at 3,017. Among the less common parameters it accepts logprobs, top_k, top_logprobs — 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-2.0 at 131,072 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.
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...
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 | 94 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 193 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 6,035 toman |
Frequently asked
- How do I call Qwen3 VL 32B 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-32b-instruct". Nothing else in your code changes.
- What does Qwen3 VL 32B Instruct cost in toman?
- 30,173 toman per 1M input tokens and 120,692 toman per 1M output tokens; a 1,000-word request is around 196 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Qwen3 VL 32B Instruct take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 32,768 tokens.
- Does Qwen3 VL 32B 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.