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

qwen/qwen3-235b-a22b-thinking-2507

Tool callingReasoning
Input · per 1M tokens
155,799 toman
Output · per 1M tokens
1,211,768 toman
One 1,000-word request ≈
1,778 toman

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

Qwen3 235B A22B Thinking 2507 example: tool calling

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-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="qwen/qwen3-235b-a22b-thinking-2507", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="qwen/qwen3-235b-a22b-thinking-2507", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

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

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "qwen/qwen3-235b-a22b-thinking-2507", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "qwen/qwen3-235b-a22b-thinking-2507", messages, tools });
console.log(final.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-thinking-2507",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is Qwen3 235B A22B Thinking 2507 good for?

The id for Qwen3 235B A22B Thinking 2507 in our API is "qwen/qwen3-235b-a22b-thinking-2507", served from Qwen (Alibaba). Context is 128,000 tokens; past that you have to summarise the history yourself. A single response can run to 16,384 tokens. On price it sits in the "mid-range" band — cheaper than 141 and dearer than 256 of the other paid models in the catalogue.

Capabilities available on this id: it supports tool calling, so it can invoke your own functions with valid arguments; 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 1,778 toman per 1,000-word exchange (155,799 in, 1,211,768 out, per 1M tokens). 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 DeepSeek V4 Pro 0423: Qwen3 235B A22B Thinking 2507 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 56 thousand-word requests on Qwen3 235B A22B Thinking 2507, and every 1,000 toman is about 562 words of round trip. A job with one million input tokens and one million output tokens comes to 1,367,566 toman in total. Filling this model's 128,000-token window costs 19,942 toman on the input side alone, which is the real reason to keep conversation history short.

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 presence_penalty, top_k — all through the standard request body. It does not support response_format, seed, structured_outputs, 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 Nova Micro 1.0 at 128,000 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases.

Provider's own description

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

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 out718 toman
Summarising a ten-page document4,000 in + 600 out1,350 toman
Classifying a thousand short rows120,000 in + 20,000 out42,931 toman

Price history

DateInput (toman/1M)Output (toman/1M)
9 Oct 2026155,7991,211,768
7 Sep 202679,630796,305

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

Frequently asked

How do I call Qwen3 235B A22B Thinking 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-thinking-2507". Nothing else in your code changes.
What does Qwen3 235B A22B Thinking 2507 cost in toman?
155,799 toman per 1M input tokens and 1,211,768 toman per 1M output tokens; a 1,000-word request is around 1,778 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 235B A22B Thinking 2507 take?
Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 16,384 tokens.
Does Qwen3 235B A22B Thinking 2507 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.