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Qwen3.5-122B-A10B API: toman pricing and code

qwen/qwen3.5-122b-a10b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
90,017 toman
Output · per 1M tokens
720,136 toman
One 1,000-word request ≈
1,053 toman

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

Qwen3.5-122B-A10B 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/qwen3.5-122b-a10b",
    "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/qwen3.5-122b-a10b",
    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/qwen3.5-122b-a10b",
  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 Qwen3.5-122B-A10B good for?

Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3.5-122b-a10b". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 184 and dearer than 212 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 90,017 toman per 1M input tokens and 720,136 per 1M output tokens. A 1,000-word round trip on Qwen3.5-122B-A10B lands near 1,053 toman. 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 Mistral Large 4: Qwen3.5-122B-A10B works out about 17% 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 95 thousand-word requests on Qwen3.5-122B-A10B, and every 1,000 toman is about 950 words of round trip. A job with one million input tokens and one million output tokens comes to 810,153 toman in total. Filling this model's 262,144-token window costs 23,597 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.5-27b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 17% cheaper (1,053 against 1,287 toman). Maximum answer length differs as well: 65,536 against 81,920 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 122B-A10B 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. In terms of...

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 out423 toman
Summarising a ten-page document4,000 in + 600 out792 toman
Classifying a thousand short rows120,000 in + 20,000 out25,205 toman

Price history

DateInput (toman/1M)Output (toman/1M)
26 Oct 202690,017720,136
23 Oct 2026138,4881,107,902
22 Oct 202690,017720,136
22 Oct 2026138,4881,107,902
22 Oct 202690,017720,136
22 Oct 2026138,4881,107,902
21 Oct 202690,017720,136
21 Oct 2026138,4881,107,902
10 Sep 202690,017720,136
7 Sep 2026100,404830,926

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

Frequently asked

How do I call Qwen3.5-122B-A10B 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/qwen3.5-122b-a10b". Nothing else in your code changes.
What does Qwen3.5-122B-A10B cost in toman?
90,017 toman per 1M input tokens and 720,136 toman per 1M output tokens; a 1,000-word request is around 1,053 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.5-122B-A10B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
Does Qwen3.5-122B-A10B 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.