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

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

Tool callingJSON output
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Input · per 1M tokens
31,160 toman
Output · per 1M tokens
380,841 toman
One 1,000-word request ≈
536 toman

You are billed for the usage the request actually reported. Prices follow the market.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)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "qwen/qwen3-next-80b-a3b-instruct", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "qwen/qwen3-next-80b-a3b-instruct", messages, tools });
console.log(final.choices[0].message.content);
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-next-80b-a3b-instruct",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

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 16,384 tokens. On price it sits in the "cheap" band — cheaper than 245 and dearer than 152 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 31,160 toman per 1M input tokens and 380,841 per 1M output tokens. A 1,000-word round trip on Qwen3 Next 80B A3B Instruct lands near 536 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 DeepSeek V3 0324: Qwen3 Next 80B A3B Instruct works out about 17% 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 187 thousand-word requests on Qwen3 Next 80B A3B Instruct, and every 1,000 toman is about 1,866 words of round trip. A job with one million input tokens and one million output tokens comes to 412,001 toman in total. Filling this model's 262,144-token window costs 8,168 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 (536 and 585 toman). Maximum answer length differs as well: 16,384 against 65,536 tokens. On parameters, this one takes logprobs, top_logprobs.

Qwen (Alibaba) has 42 models in our catalogue; the cheapest is Qwen3.7 Flash at 72 toman per thousand words and the dearest Qwen3.8 Max Prime at 7,201. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — 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 Aion 3.5 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 out199 toman
Summarising a ten-page document4,000 in + 600 out353 toman
Classifying a thousand short rows120,000 in + 20,000 out11,356 toman

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202631,160380,841
4 Nov 202634,622380,841
21 Oct 202631,160380,841
20 Oct 202634,622380,841
16 Oct 202631,160380,841
9 Oct 202634,622380,841
8 Oct 202631,160380,841
8 Oct 202651,933519,329
24 Sep 202634,622380,841
9 Sep 202631,160380,841
7 Sep 202634,622380,841

Every price change for this model is recorded. Toman figures use today's rate.

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?
31,160 toman per 1M input tokens and 380,841 toman per 1M output tokens; a 1,000-word request is around 536 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 16,384 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.