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Qwen3 Next 80B A3B Instruct API: toman pricing and code

qwen/qwen3-next-80b-a3b-instruct

toolsjson
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Input · per 1M tokens
29,013 toman
Output · per 1M tokens
319,138 toman
One 1,000-word request ≈
453 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 20,309 toman / 1M.Pricing and top-ups

Qwen3 Next 80B A3B Instruct 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-next-80b-a3b-instruct", 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-next-80b-a3b-instruct", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Qwen3 Next 80B A3B Instruct good for?

The id for Qwen3 Next 80B A3B Instruct in our API is "qwen/qwen3-next-80b-a3b-instruct", served from Qwen (Alibaba). Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 235,929 tokens. On price it sits in the "cheap" band — cheaper than 146 and dearer than 260 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 29,013 toman per 1M input tokens and 319,138 per 1M output tokens. A 1,000-word round trip on Qwen3 Next 80B A3B Instruct lands near 453 toman. It supports cached input: repeated context is billed at 20,309 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 MiniMax M2.5: Qwen3 Next 80B A3B Instruct works out roughly 1.1× cheaper, 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 221 thousand-word requests on Qwen3 Next 80B A3B Instruct, and every 1,000 toman is about 2,208 words of round trip. A job with one million input tokens and one million output tokens comes to 348,150 toman in total. Filling this model's 262,144-token window costs 7,605 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", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (453 and 490 toman). Maximum answer length differs as well: 235,929 against 65,536 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-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...

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 out171 toman
Summarising a ten-page document4,000 in + 600 out308 toman
Classifying a thousand short rows120,000 in + 20,000 out9,864 toman

Frequently asked

How do I call Qwen3 Next 80B 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-next-80b-a3b-instruct". Nothing else in your code changes.
What does Qwen3 Next 80B A3B Instruct cost in toman?
29,013 toman per 1M input tokens and 319,138 toman per 1M output tokens; a 1,000-word request is around 453 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Next 80B A3B Instruct take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3 Next 80B 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.