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Qwen3 30B A3B API: toman pricing and code

qwen/qwen3-30b-a3b

Tool callingReasoningJSON output
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Input · per 1M tokens
41,546 toman
Output · per 1M tokens
173,110 toman
One 1,000-word request ≈
279 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Qwen3 30B A3B 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/qwen3-30b-a3b",
    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))
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const resp = await client.chat.completions.create({
  model: "qwen/qwen3-30b-a3b",
  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: {
      name: "ticket",
      strict: true,
      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,
      },
    },
  },
});
console.log(JSON.parse(resp.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",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is Qwen3 30B A3B good for?

Qwen3 30B A3B comes from Qwen (Alibaba); in uttapen you reach it with the model id "qwen/qwen3-30b-a3b". It accepts up to 40,960 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 300 and dearer than 85 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; 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.

Pricing is 41,546 toman per 1M input tokens and 173,110 per 1M output tokens. A 1,000-word round trip on Qwen3 30B A3B lands near 279 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 Llama 3.3 70B Instruct: Qwen3 30B A3B works out about 17% 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 358 thousand-word requests on Qwen3 30B A3B, and every 1,000 toman is about 3,584 words of round trip. A job with one million input tokens and one million output tokens comes to 214,656 toman in total. Filling this model's 40,960-token window costs 1,702 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3-14b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 70% more expensive (279 against 162 toman). On parameters the other takes logprobs, top_logprobs.

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, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Perceptron Mk1.5 at 36,864 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...

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 out132 toman
Summarising a ten-page document4,000 in + 600 out270 toman
Classifying a thousand short rows120,000 in + 20,000 out8,448 toman

Price history

DateInput (toman/1M)Output (toman/1M)
1 Oct 202641,546173,110
1 Oct 202645,009180,034
30 Sep 202641,546173,110
30 Sep 202645,009180,034
29 Sep 202641,546173,110
29 Sep 202645,009180,034
28 Sep 202641,546173,110
28 Sep 202645,009180,034
27 Sep 202641,546173,110
27 Sep 202645,009180,034
27 Sep 202641,546173,110
27 Sep 202645,009180,034

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

Frequently asked

How do I call Qwen3 30B A3B 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". Nothing else in your code changes.
What does Qwen3 30B A3B cost in toman?
41,546 toman per 1M input tokens and 173,110 toman per 1M output tokens; a 1,000-word request is around 279 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 30B A3B take?
Up to 40,960 tokens per request, roughly 31k words. A single answer can reach 16,384 tokens.
Does Qwen3 30B A3B 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.