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

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

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Input · per 1M tokens
20,309 toman
Output · per 1M tokens
81,235 toman
One 1,000-word request ≈
132 toman

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

Qwen3 Coder 30B A3B Instruct 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-coder-30b-a3b-instruct",
    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 Coder 30B A3B Instruct good for?

Qwen3 Coder 30B A3B Instruct is one of Qwen (Alibaba)'s models. Put "qwen/qwen3-coder-30b-a3b-instruct" in the model field and the rest of your code stays as it is. Qwen3 Coder 30B A3B Instruct keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 235,929 tokens. On price it sits in the "very cheap" band — cheaper than 54 and dearer than 352 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 132 toman per 1,000-word exchange (20,309 in, 81,235 out, per 1M tokens). 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 Coder 30B A3B Instruct works out roughly 1.2× 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 758 thousand-word requests on Qwen3 Coder 30B A3B Instruct, and every 1,000 toman is about 7,576 words of round trip. A job with one million input tokens and one million output tokens comes to 101,544 toman in total. Filling this model's 262,144-token window costs 5,324 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3-30b-a3b-instruct-2507", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.5× dearer (132 against 91 toman). Maximum answer length differs as well: 235,929 against 32,000 tokens. On parameters the other 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 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.

What to use it for: 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-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...

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 out63 toman
Summarising a ten-page document4,000 in + 600 out130 toman
Classifying a thousand short rows120,000 in + 20,000 out4,062 toman

Frequently asked

How do I call Qwen3 Coder 30B A3B Instruct 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-coder-30b-a3b-instruct". Nothing else in your code changes.
What does Qwen3 Coder 30B A3B Instruct cost in toman?
20,309 toman per 1M input tokens and 81,235 toman per 1M output tokens; a 1,000-word request is around 132 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Coder 30B A3B Instruct take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3 Coder 30B A3B Instruct 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.