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Qwen-Plus API: toman pricing and code

qwen/qwen-plus

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Input · per 1M tokens
75,433 toman
Output · per 1M tokens
226,298 toman
One 1,000-word request ≈
392 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 15,087 toman / 1M.Pricing and top-ups

Qwen-Plus 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/qwen-plus",
    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))

What is Qwen-Plus good for?

Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen-plus". Its context window is 1,000,000 tokens, roughly 750k English words in one request. A single response can run to 32,768 tokens. On price it sits in the "cheap" band — cheaper than 118 and dearer than 288 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 75,433 toman per 1M tokens and output at 226,298 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 15,087 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 Trinity Large Thinking: Qwen-Plus works out roughly 1× 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 255 thousand-word requests on Qwen-Plus, and every 1,000 toman is about 2,551 words of round trip. A job with one million input tokens and one million output tokens comes to 301,730 toman in total. Filling this model's 1,000,000-token window costs 75,433 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen-plus-2025-07-28", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (392 and 392 toman).

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 Nova 2 Lite at 1,000,000 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

Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.

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 out204 toman
Summarising a ten-page document4,000 in + 600 out438 toman
Classifying a thousand short rows120,000 in + 20,000 out13,578 toman

Frequently asked

How do I call Qwen-Plus 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/qwen-plus". Nothing else in your code changes.
What does Qwen-Plus cost in toman?
75,433 toman per 1M input tokens and 226,298 toman per 1M output tokens; a 1,000-word request is around 392 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen-Plus take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 32,768 tokens.
Does Qwen-Plus 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.