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Qwen3 Max API: toman pricing and code

qwen/qwen3-max

toolsjson
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Input · per 1M tokens
226,298 toman
Output · per 1M tokens
1,131,488 toman
One 1,000-word request ≈
1,765 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 45,260 toman / 1M.Pricing and top-ups

Qwen3 Max 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-max", 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-max", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Qwen3 Max good for?

The id for Qwen3 Max in our API is "qwen/qwen3-max", served from Qwen (Alibaba). Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 262 and dearer than 144 of the other paid models in the catalogue.

What it can do beyond plain text: 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.

Pricing is 226,298 toman per 1M input tokens and 1,131,488 per 1M output tokens. A 1,000-word round trip on Qwen3 Max lands near 1,765 toman. It supports cached input: repeated context is billed at 45,260 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 DeepSeek V4 Pro 0813 (batch): Qwen3 Max works out roughly 1.1× 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 57 thousand-word requests on Qwen3 Max, and every 1,000 toman is about 567 words of round trip. A job with one million input tokens and one million output tokens comes to 1,357,785 toman in total. Filling this model's 262,144-token window costs 59,323 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", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 3.6× dearer (1,765 against 490 toman). On parameters the other takes logit_bias, min_p, repetition_penalty.

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, top_k, top_logprobs — 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 Trinity Large Thinking at 262,144 tokens.

Good fits: product chatbots and internal assistants, where cost and quality have to balance, 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-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It...

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 out792 toman
Summarising a ten-page document4,000 in + 600 out1,584 toman
Classifying a thousand short rows120,000 in + 20,000 out49,785 toman

Frequently asked

How do I call Qwen3 Max 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-max". Nothing else in your code changes.
What does Qwen3 Max cost in toman?
226,298 toman per 1M input tokens and 1,131,488 toman per 1M output tokens; a 1,000-word request is around 1,765 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Max take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
Does Qwen3 Max 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.