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Qwen3.8 27B API: toman pricing and code

qwen/qwen3.8-27b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
649,161 toman
One 1,000-word request ≈
911 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 12,983 toman per 1M tokens.Pricing and top-ups

Qwen3.8 27B 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/qwen3.8-27b",
    "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/qwen3.8-27b",
    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/qwen3.8-27b",
  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 27B good for?

Qwen3.8 27B is one of Qwen (Alibaba)'s models. Put "qwen/qwen3.8-27b" in the model field and the rest of your code stays as it is. Qwen3.8 27B keeps 1,024,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 235,929 tokens. On price it sits in the "cheap" band — cheaper than 202 and dearer than 192 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; 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.

Input runs at 51,933 toman per 1M tokens and output at 649,161 — output costs 12.5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 12,983 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 Gemini 3.6 Flash (batch): Qwen3.8 27B works out about 9% cheaper, 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 110 thousand-word requests on Qwen3.8 27B, and every 1,000 toman is about 1,098 words of round trip. A job with one million input tokens and one million output tokens comes to 701,094 toman in total. Filling this model's 1,024,000-token window costs 53,179 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.5-plus-20260420", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (911 and 945 toman). The context windows differ too: 1,024,000 against 1,000,000 tokens. Maximum answer length differs as well: 235,929 against 65,536 tokens. On parameters, this one takes logit_bias, min_p, reasoning_effort, repetition_penalty.

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, reasoning_effort, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is UnslopNemo 12B at 1,024,000 tokens.

Where it makes sense: 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 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...

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 out338 toman
Summarising a ten-page document4,000 in + 600 out597 toman
Classifying a thousand short rows120,000 in + 20,000 out19,215 toman

Price history

DateInput (toman/1M)Output (toman/1M)
4 Nov 202651,933649,161
31 Oct 202613,849796,305
31 Oct 202651,933649,161
30 Oct 202669,244865,548
29 Oct 202613,503432,774
29 Oct 202669,244865,548
28 Oct 202613,503432,774
27 Oct 202669,244865,548
27 Oct 202613,503432,774
23 Oct 202669,244865,548
20 Oct 202613,849467,396
15 Oct 202683,093761,683

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

Frequently asked

How do I call Qwen3.8 27B 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/qwen3.8-27b". Nothing else in your code changes.
What does Qwen3.8 27B cost in toman?
51,933 toman per 1M input tokens and 649,161 toman per 1M output tokens; a 1,000-word request is around 911 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.8 27B take?
Up to 1,024,000 tokens per request, roughly 768k words. A single answer can reach 235,929 tokens.
Does Qwen3.8 27B 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.