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

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

toolsreasoningjson
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Input · per 1M tokens
66,729 toman
Output · per 1M tokens
667,288 toman
One 1,000-word request ≈
954 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)

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 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 117,964 tokens. On price it sits in the "mid-range" band — cheaper than 221 and dearer than 185 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 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.

For a back-of-envelope figure: about 954 toman per 1,000-word exchange (66,729 in, 667,288 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 Kimi K2 0711: Qwen3 235B A22B Thinking 2507 works out roughly 1.1× cheaper, 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 105 thousand-word requests on Qwen3 235B A22B Thinking 2507, and every 1,000 toman is about 1,048 words of round trip. A job with one million input tokens and one million output tokens comes to 734,016 toman in total. Filling this model's 131,072-token window costs 8,746 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3-235b-a22b", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (954 and 858 toman). Maximum answer length differs as well: 117,964 against 8,192 tokens. On parameters, this one takes logprobs, repetition_penalty, top_logprobs.

Qwen (Alibaba) has 53 models in our catalogue; the cheapest is Qwen3.7 Flash at 60 toman per thousand words and the dearest Qwen3.8 Max (0902) at 3,017. Among the less common parameters it accepts logprobs, repetition_penalty, top_k, top_logprobs — all through the standard request body. It does not support 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 Aion-2.0 at 131,072 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, extracting data against a fixed schema.

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 out367 toman
Summarising a ten-page document4,000 in + 600 out667 toman
Classifying a thousand short rows120,000 in + 20,000 out21,353 toman

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?
66,729 toman per 1M input tokens and 667,288 toman per 1M output tokens; a 1,000-word request is around 954 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 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 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. Structured output through response_format works too.