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

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

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Input · per 1M tokens
13,970 toman
Output · per 1M tokens
56,009 toman
One 1,000-word request ≈
91 toman

You are billed for the usage the request actually reported. Prices follow the market.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)

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 32,000 tokens. On price it sits in the "very cheap" band — cheaper than 32 and dearer than 374 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 13,970 toman per 1M input tokens and 56,009 per 1M output tokens. A 1,000-word round trip on Qwen3 30B A3B Instruct 2507 lands near 91 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 Nemotron 3 Nano 30B A3B: Qwen3 30B A3B Instruct 2507 works out roughly 1× cheaper, and its context window is the same size. 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 1,099 thousand-word requests on Qwen3 30B A3B Instruct 2507, and every 1,000 toman is about 10,989 words of round trip. A job with one million input tokens and one million output tokens comes to 69,978 toman in total. Filling this model's 262,144-token window costs 3,662 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-coder-30b-a3b-instruct", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.5× cheaper (91 against 132 toman). Maximum answer length differs as well: 32,000 against 235,929 tokens. On parameters, this one takes logit_bias.

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, logprobs, 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 Trinity Large Thinking 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 out43 toman
Summarising a ten-page document4,000 in + 600 out89 toman
Classifying a thousand short rows120,000 in + 20,000 out2,797 toman

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?
13,970 toman per 1M input tokens and 56,009 toman per 1M output tokens; a 1,000-word request is around 91 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 32,000 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.