Qwen3.6 Flash API: toman pricing and code
qwen/qwen3.6-flash
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Qwen3.6 Flash 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-flash",
"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-flash",
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-flash",
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 Flash good for?
The id for Qwen3.6 Flash in our API is "qwen/qwen3.6-flash", served from Qwen (Alibaba). Context is 1,000,000 tokens; past that you have to summarise the history yourself. A single response can run to 65,536 tokens. On price it sits in the "cheap" band — cheaper than 149 and dearer than 257 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 495 toman per 1,000-word exchange (54,398 in, 326,391 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 Muse Glimmer 30B: Qwen3.6 Flash works out roughly 1.1× cheaper, and its context window is larger. 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 202 thousand-word requests on Qwen3.6 Flash, and every 1,000 toman is about 2,020 words of round trip. A job with one million input tokens and one million output tokens comes to 380,789 toman in total. Filling this model's 1,000,000-token window costs 54,398 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "qwen/qwen3.7-plus", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.2× cheaper (495 against 603 toman). Maximum answer length differs as well: 65,536 against 131,072 tokens.
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. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 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.
Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...
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 | 212 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 413 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 13,056 toman |
Frequently asked
- How do I call Qwen3.6 Flash 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-flash". Nothing else in your code changes.
- What does Qwen3.6 Flash cost in toman?
- 54,398 toman per 1M input tokens and 326,391 toman per 1M output tokens; a 1,000-word request is around 495 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Qwen3.6 Flash take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,536 tokens.
- Does Qwen3.6 Flash 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.