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Qwen3 Coder Next API: toman pricing and code

qwen/qwen3-coder-next

Tool callingJSON output
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Input · per 1M tokens
41,546 toman
Output · per 1M tokens
276,975 toman
One 1,000-word request ≈
414 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 24,235 toman per 1M tokens.Pricing and top-ups

Qwen3 Coder Next 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-next",
    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))
import OpenAI from "openai";

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

const resp = await client.chat.completions.create({
  model: "qwen/qwen3-coder-next",
  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: {
      name: "ticket",
      strict: true,
      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,
      },
    },
  },
});
console.log(JSON.parse(resp.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-coder-next",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is Qwen3 Coder Next good for?

Qwen3 Coder Next is one of Qwen (Alibaba)'s models. Put "qwen/qwen3-coder-next" in the model field and the rest of your code stays as it is. Qwen3 Coder Next 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 "cheap" band — cheaper than 268 and dearer than 128 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 41,546 toman per 1M tokens and output at 276,975 — output costs 6.7× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 24,235 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 Mercury 2: Qwen3 Coder Next works out about 9% 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 242 thousand-word requests on Qwen3 Coder Next, and every 1,000 toman is about 2,415 words of round trip. A job with one million input tokens and one million output tokens comes to 318,522 toman in total. Filling this model's 262,144-token window costs 10,891 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 about 20% more expensive (414 against 338 toman). On parameters, this one takes logit_bias, logprobs, presence_penalty, repetition_penalty.

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, presence_penalty, 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 Aion 3.5 at 262,144 tokens.

Where it makes sense: 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-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...

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 out173 toman
Summarising a ten-page document4,000 in + 600 out332 toman
Classifying a thousand short rows120,000 in + 20,000 out10,525 toman

Frequently asked

How do I call Qwen3 Coder Next 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-next". Nothing else in your code changes.
What does Qwen3 Coder Next cost in toman?
41,546 toman per 1M input tokens and 276,975 toman per 1M output tokens; a 1,000-word request is around 414 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Coder Next take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Qwen3 Coder Next 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.