uttapen
Create an API key

Qwen3 Coder 480B A35B API: toman pricing and code

qwen/qwen3-coder

toolsjson
Create a keyTry it
Input · per 1M tokens
87,038 toman
Output · per 1M tokens
290,125 toman
One 1,000-word request ≈
490 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 29,013 toman / 1M.Pricing and top-ups

Qwen3 Coder 480B A35B 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",
    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 480B A35B good for?

Qwen3 Coder 480B A35B is one of Qwen (Alibaba)'s models. Put "qwen/qwen3-coder" in the model field and the rest of your code stays as it is. Qwen3 Coder 480B A35B 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 65,536 tokens. On price it sits in the "cheap" band — cheaper than 135 and dearer than 271 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 87,038 toman per 1M input tokens and 290,125 per 1M output tokens. A 1,000-word round trip on Qwen3 Coder 480B A35B lands near 490 toman. It supports cached input: repeated context is billed at 29,013 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 V3 0324: Qwen3 Coder 480B A35B works out roughly 1× more expensive, and its context window is larger. 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 204 thousand-word requests on Qwen3 Coder 480B A35B, and every 1,000 toman is about 2,041 words of round trip. A job with one million input tokens and one million output tokens comes to 377,163 toman in total. Filling this model's 262,144-token window costs 22,816 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-next-80b-a3b-instruct", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (490 and 453 toman). Maximum answer length differs as well: 65,536 against 235,929 tokens.

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, min_p, 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-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...

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 out247 toman
Summarising a ten-page document4,000 in + 600 out522 toman
Classifying a thousand short rows120,000 in + 20,000 out16,247 toman

Frequently asked

How do I call Qwen3 Coder 480B A35B 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". Nothing else in your code changes.
What does Qwen3 Coder 480B A35B cost in toman?
87,038 toman per 1M input tokens and 290,125 toman per 1M output tokens; a 1,000-word request is around 490 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Coder 480B A35B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
Does Qwen3 Coder 480B A35B 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.