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Qwen3 Coder Flash API: toman pricing and code

qwen/qwen3-coder-flash

Tool callingJSON output
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Input · per 1M tokens
67,513 toman
Output · per 1M tokens
337,564 toman
One 1,000-word request ≈
527 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 13,503 toman per 1M tokens.Pricing and top-ups

Qwen3 Coder Flash 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-coder-flash", 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-coder-flash", 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-coder-flash", 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-coder-flash", 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-coder-flash",
    "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 Coder Flash good for?

The id for Qwen3 Coder Flash in our API is "qwen/qwen3-coder-flash", served from Qwen (Alibaba). Context is 1,000,000 tokens; past that you have to summarise the history yourself. A single response can run to 65,536 tokens. On price it sits in the "cheap" band — cheaper than 257 and dearer than 139 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 67,513 toman per 1M input tokens and 337,564 per 1M output tokens. A 1,000-word round trip on Qwen3 Coder Flash lands near 527 toman. It supports cached input: repeated context is billed at 13,503 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 V3.1, and Qwen3 Coder Flash costs about the same, 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 190 thousand-word requests on Qwen3 Coder Flash, and every 1,000 toman is about 1,898 words of round trip. A job with one million input tokens and one million output tokens comes to 405,077 toman in total. Filling this model's 1,000,000-token window costs 67,513 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen-plus", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (527 and 468 toman). Maximum answer length differs as well: 65,536 against 32,768 tokens. On parameters the other takes structured_outputs.

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 logprobs, presence_penalty, top_k, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, structured_outputs, 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 Nova 2 Lite at 1,000,000 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 Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling...

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 out236 toman
Summarising a ten-page document4,000 in + 600 out473 toman
Classifying a thousand short rows120,000 in + 20,000 out14,853 toman

Frequently asked

How do I call Qwen3 Coder Flash 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-flash". Nothing else in your code changes.
What does Qwen3 Coder Flash cost in toman?
67,513 toman per 1M input tokens and 337,564 toman per 1M output tokens; a 1,000-word request is around 527 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 Coder Flash take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,536 tokens.
Does Qwen3 Coder Flash 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.