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Qwen3.5 397B A17B API: toman pricing and code

qwen/qwen3.5-397b-a17b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
173,110 toman
Output · per 1M tokens
1,246,390 toman
One 1,000-word request ≈
1,845 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 103,866 toman per 1M tokens.Pricing and top-ups

Qwen3.5 397B A17B 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/qwen3.5-397b-a17b",
    "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/qwen3.5-397b-a17b",
    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/qwen3.5-397b-a17b",
  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 397B A17B good for?

The id for Qwen3.5 397B A17B in our API is "qwen/qwen3.5-397b-a17b", served from Qwen (Alibaba). Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 235,929 tokens. On price it sits in the "mid-range" band — cheaper than 140 and dearer than 258 of the other paid models in the catalogue.

What it can do beyond plain text: 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.

Pricing is 173,110 toman per 1M input tokens and 1,246,390 per 1M output tokens. A 1,000-word round trip on Qwen3.5 397B A17B lands near 1,845 toman. It supports cached input: repeated context is billed at 103,866 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 Kimi K2.7 Code, and Qwen3.5 397B A17B 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 54 thousand-word requests on Qwen3.5 397B A17B, and every 1,000 toman is about 542 words of round trip. A job with one million input tokens and one million output tokens comes to 1,419,499 toman in total. Filling this model's 262,144-token window costs 45,380 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.6-27b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 30% more expensive (1,845 against 1,418 toman). Maximum answer length differs as well: 235,929 against 65,536 tokens.

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, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Good fits: product chatbots and internal assistants, where cost and quality have to balance, 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 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...

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 out758 toman
Summarising a ten-page document4,000 in + 600 out1,440 toman
Classifying a thousand short rows120,000 in + 20,000 out45,701 toman

Price history

DateInput (toman/1M)Output (toman/1M)
22 Oct 2026173,1101,246,390
20 Oct 2026135,026810,153
14 Oct 2026173,1101,246,390
9 Oct 2026190,4211,211,768
6 Oct 2026155,7991,038,658
8 Sep 2026190,4211,211,768
7 Sep 2026135,026810,153
7 Sep 2026190,4211,211,768

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

Frequently asked

How do I call Qwen3.5 397B A17B 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/qwen3.5-397b-a17b". Nothing else in your code changes.
What does Qwen3.5 397B A17B cost in toman?
173,110 toman per 1M input tokens and 1,246,390 toman per 1M output tokens; a 1,000-word request is around 1,845 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.5 397B A17B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3.5 397B A17B 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.