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Qwen3.5-Flash API: toman pricing and code

qwen/qwen3.5-flash-02-23

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
22,504 toman
Output · per 1M tokens
90,017 toman
One 1,000-word request ≈
146 toman

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

Qwen3.5-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/qwen3.5-flash-02-23",
    "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-flash-02-23",
    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-flash-02-23",
  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-Flash good for?

Qwen3.5-Flash comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3.5-flash-02-23". It accepts up to 1,000,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 65,536 tokens. On price it sits in the "very cheap" band — cheaper than 341 and dearer than 57 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 146 toman per 1,000-word exchange (22,504 in, 90,017 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-Flash works out about 10% more expensive, and its context window is the same size. 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 685 thousand-word requests on Qwen3.5-Flash, and every 1,000 toman is about 6,849 words of round trip. A job with one million input tokens and one million output tokens comes to 112,521 toman in total. Filling this model's 1,000,000-token window costs 22,504 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-flash", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2× more expensive (146 against 72 toman). On parameters, this one takes frequency_penalty, stop, structured_outputs, top_k while the other takes logprobs, top_logprobs.

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 presence_penalty, top_k — 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.

Provider's own description

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...

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 out70 toman
Summarising a ten-page document4,000 in + 600 out144 toman
Classifying a thousand short rows120,000 in + 20,000 out4,501 toman

Frequently asked

How do I call Qwen3.5-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/qwen3.5-flash-02-23". Nothing else in your code changes.
What does Qwen3.5-Flash cost in toman?
22,504 toman per 1M input tokens and 90,017 toman per 1M output tokens; a 1,000-word request is around 146 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.5-Flash take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,536 tokens.
Does Qwen3.5-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.