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Qwen3.8 Omni Flash API: toman pricing and code

qwen/qwen3.8-omni-flash

Image inputTool callingReasoningJSON outputAudio input
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
162,723 toman
One 1,000-word request ≈
279 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,540 toman per 1M tokens.Pricing and top-ups

Qwen3.8 Omni 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/qwen3.8-omni-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/qwen3.8-omni-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/qwen3.8-omni-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 Qwen3.8 Omni Flash good for?

Qwen3.8 Omni Flash comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3.8-omni-flash". 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 131,072 tokens. On price it sits in the "very cheap" band — cheaper than 313 and dearer than 83 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 accepts audio input; 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 279 toman per 1,000-word exchange (51,933 in, 162,723 out, per 1M tokens). It supports cached input: repeated context is billed at 5,540 toman per 1M, which matters a lot if your system prompt is long. 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, and Qwen3.8 Omni Flash costs about the same, 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 358 thousand-word requests on Qwen3.8 Omni Flash, and every 1,000 toman is about 3,584 words of round trip. A job with one million input tokens and one million output tokens comes to 214,656 toman in total. Filling this model's 1,000,000-token window costs 51,933 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.8-flash", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (279 and 279 toman).

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 logprobs, presence_penalty, 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.

Provider's own description

Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization,...

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 out143 toman
Summarising a ten-page document4,000 in + 600 out305 toman
Classifying a thousand short rows120,000 in + 20,000 out9,486 toman

Frequently asked

How do I call Qwen3.8 Omni 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/qwen3.8-omni-flash". Nothing else in your code changes.
What does Qwen3.8 Omni Flash cost in toman?
51,933 toman per 1M input tokens and 162,723 toman per 1M output tokens; a 1,000-word request is around 279 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.8 Omni Flash take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 131,072 tokens.
Does Qwen3.8 Omni 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.