Qwen Plus 0728 API: toman pricing and code
qwen/qwen-plus-2025-07-28
toolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Qwen Plus 0728 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/qwen-plus-2025-07-28", 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/qwen-plus-2025-07-28", 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/qwen-plus-2025-07-28", 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/qwen-plus-2025-07-28", 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/qwen-plus-2025-07-28",
"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 Qwen Plus 0728 good for?
The id for Qwen Plus 0728 in our API is "qwen/qwen-plus-2025-07-28", served from Qwen (Alibaba). Context is 1,000,000 tokens; past that you have to summarise the history yourself. 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 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 75,433 toman per 1M input tokens and 226,298 per 1M output tokens. A 1,000-word round trip on Qwen Plus 0728 lands near 392 toman. 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 0728 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 0728, 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", 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.
Good fits: 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.
Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning 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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 204 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 438 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 13,578 toman |
Frequently asked
- How do I call Qwen Plus 0728 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-2025-07-28". Nothing else in your code changes.
- What does Qwen Plus 0728 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 0728 take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 32,768 tokens.
- Does Qwen Plus 0728 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.