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Qwen3 30B A3B API: toman pricing and code

qwen/qwen3-30b-a3b

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Input · per 1M tokens
34,815 toman
Output · per 1M tokens
145,063 toman
One 1,000-word request ≈
234 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))

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 131,072 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 88 and dearer than 318 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 34,815 toman per 1M input tokens and 145,063 per 1M output tokens. A 1,000-word round trip on Qwen3 30B A3B lands near 234 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 Hy3: Qwen3 30B A3B 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 427 thousand-word requests on Qwen3 30B A3B, and every 1,000 toman is about 4,274 words of round trip. A job with one million input tokens and one million output tokens comes to 179,878 toman in total. Filling this model's 131,072-token window costs 4,563 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3-8b", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (234 and 216 toman). Maximum answer length differs as well: 16,384 against 8,192 tokens. On parameters, this one 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 logit_bias, min_p, repetition_penalty, top_k — all through the standard request body. It does not support structured_outputs, 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-2.0 at 131,072 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 out110 toman
Summarising a ten-page document4,000 in + 600 out226 toman
Classifying a thousand short rows120,000 in + 20,000 out7,079 toman

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?
34,815 toman per 1M input tokens and 145,063 toman per 1M output tokens; a 1,000-word request is around 234 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 131,072 tokens per request, roughly 98k 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.