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Qwen3 235B A22B Instruct 2507 API: toman pricing and code

qwen/qwen3-235b-a22b-2507

Tool callingJSON output
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Input · per 1M tokens
48,471 toman
Output · per 1M tokens
276,975 toman
One 1,000-word request ≈
423 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 17,311 toman per 1M tokens.Pricing and top-ups

Qwen3 235B A22B Instruct 2507 example: JSON output against a schema

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="qwen/qwen3-235b-a22b-2507",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const resp = await client.chat.completions.create({
  model: "qwen/qwen3-235b-a22b-2507",
  messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "ticket",
      strict: true,
      schema: {
        type: "object",
        properties: {
          category: { type: "string", enum: ["fani", "mali", "forush"] },
          priority: { type: "integer", minimum: 1, maximum: 5 },
          summary: { type: "string" },
        },
        required: ["category", "priority", "summary"],
        additionalProperties: false,
      },
    },
  },
});
console.log(JSON.parse(resp.choices[0].message.content));
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-235b-a22b-2507",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is Qwen3 235B A22B Instruct 2507 good for?

Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3-235b-a22b-2507". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 117,964 tokens. On price it sits in the "cheap" band — cheaper than 269 and dearer than 127 of the other paid models in the catalogue.

What you get on top of text in, text out: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Input runs at 48,471 toman per 1M tokens and output at 276,975 — output costs 5.7× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 17,311 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 Mercury 2: Qwen3 235B A22B Instruct 2507 works out about 9% cheaper, and its context window is larger. 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 236 thousand-word requests on Qwen3 235B A22B Instruct 2507, and every 1,000 toman is about 2,364 words of round trip. A job with one million input tokens and one million output tokens comes to 325,446 toman in total. Filling this model's 262,144-token window costs 12,706 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-coder-next", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (423 and 414 toman). Maximum answer length differs as well: 117,964 against 235,929 tokens. On parameters, this one takes min_p.

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. It does not support include_reasoning, reasoning, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, 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

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...

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 out183 toman
Summarising a ten-page document4,000 in + 600 out360 toman
Classifying a thousand short rows120,000 in + 20,000 out11,356 toman

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202648,471276,975
5 Nov 202631,160190,421
5 Nov 202648,471276,975
4 Nov 202631,160190,421
4 Nov 202648,471276,975
6 Oct 202631,160190,421
12 Sep 202630,294121,177
9 Sep 202676,168304,673
7 Sep 202631,160190,421

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

Frequently asked

How do I call Qwen3 235B A22B Instruct 2507 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/qwen3-235b-a22b-2507". Nothing else in your code changes.
What does Qwen3 235B A22B Instruct 2507 cost in toman?
48,471 toman per 1M input tokens and 276,975 toman per 1M output tokens; a 1,000-word request is around 423 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 235B A22B Instruct 2507 take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 117,964 tokens.
Does Qwen3 235B A22B Instruct 2507 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.