Qwen3.6 Max Preview API: toman pricing and code
qwen/qwen3.6-max-preview
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Qwen3.6 Max Preview example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="qwen/qwen3.6-max-preview",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.6-max-preview",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'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/qwen3.6-max-preview",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is Qwen3.6 Max Preview good for?
Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3.6-max-preview". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 311 and dearer than 95 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; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Input runs at 297,958 toman per 1M tokens and output at 1,787,750 — output costs 6× input, so trimming the answer saves more than trimming the prompt. 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 Aion-3.0: Qwen3.6 Max Preview works out roughly 1.3× 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 37 thousand-word requests on Qwen3.6 Max Preview, and every 1,000 toman is about 369 words of round trip. A job with one million input tokens and one million output tokens comes to 2,085,709 toman in total. Filling this model's 262,144-token window costs 78,108 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-max-thinking", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.5× dearer (2,711 against 1,765 toman).
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 logprobs, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 1,162 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,264 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 71,510 toman |
Frequently asked
- How do I call Qwen3.6 Max Preview 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/qwen3.6-max-preview". Nothing else in your code changes.
- What does Qwen3.6 Max Preview cost in toman?
- 297,958 toman per 1M input tokens and 1,787,750 toman per 1M output tokens; a 1,000-word request is around 2,711 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Qwen3.6 Max Preview take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
- Does Qwen3.6 Max Preview 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.