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Qwen3 30B A3B Instruct 2507 API: toman pricing and code

qwen/qwen3-30b-a3b-instruct-2507

Tool callingJSON output
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Input · per 1M tokens
34,622 toman
Output · per 1M tokens
103,866 toman
One 1,000-word request ≈
180 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,622 toman per 1M tokens.Pricing and top-ups

Qwen3 30B A3B Instruct 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/qwen3-30b-a3b-instruct-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/qwen3-30b-a3b-instruct-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/qwen3-30b-a3b-instruct-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/qwen3-30b-a3b-instruct-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/qwen3-30b-a3b-instruct-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 30B A3B Instruct 2507 good for?

Qwen3 30B A3B Instruct 2507 comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3-30b-a3b-instruct-2507". It accepts up to 262,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 235,929 tokens. On price it sits in the "very cheap" band — cheaper than 327 and dearer than 61 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 34,622 toman per 1M input tokens and 103,866 per 1M output tokens. A 1,000-word round trip on Qwen3 30B A3B Instruct 2507 lands near 180 toman. It supports cached input: repeated context is billed at 34,622 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 Flash 0731: Qwen3 30B A3B Instruct 2507 works out about 10% more expensive, 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 556 thousand-word requests on Qwen3 30B A3B Instruct 2507, and every 1,000 toman is about 5,556 words of round trip. A job with one million input tokens and one million output tokens comes to 138,488 toman in total. Filling this model's 262,144-token window costs 9,076 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-2507", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 38% cheaper (180 against 288 toman). Maximum answer length differs as well: 235,929 against 16,384 tokens. On parameters the other takes min_p.

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 logit_bias, logprobs, presence_penalty, repetition_penalty, 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 Aion 3.5 at 262,144 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.

Provider's own description

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...

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 out93 toman
Summarising a ten-page document4,000 in + 600 out201 toman
Classifying a thousand short rows120,000 in + 20,000 out6,232 toman

Price history

DateInput (toman/1M)Output (toman/1M)
27 Oct 202634,622103,866
26 Oct 202634,622103,866
18 Oct 202631,160103,866
8 Oct 202634,622103,866
8 Oct 202616,67066,838
8 Oct 202634,622103,866
7 Oct 202616,67066,838
6 Oct 202634,622103,866
3 Oct 202616,67066,838
3 Oct 202634,622103,866
2 Oct 202616,67066,838
2 Oct 202634,622103,866

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

Frequently asked

How do I call Qwen3 30B A3B Instruct 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/qwen3-30b-a3b-instruct-2507". Nothing else in your code changes.
What does Qwen3 30B A3B Instruct 2507 cost in toman?
34,622 toman per 1M input tokens and 103,866 toman per 1M output tokens; a 1,000-word request is around 180 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 30B A3B Instruct 2507 take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3 30B A3B Instruct 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.