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Qwen3.7 Max API: toman pricing and code

qwen/qwen3.7-max

Tool callingReasoningJSON output
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Input · per 1M tokens
510,674 toman
Output · per 1M tokens
1,532,021 toman
One 1,000-word request ≈
2,656 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 102,135 toman per 1M tokens.Pricing and top-ups

Qwen3.7 Max 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/qwen3.7-max", 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/qwen3.7-max", 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/qwen3.7-max", 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/qwen3.7-max", 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/qwen3.7-max",
    "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 Qwen3.7 Max good for?

Qwen3.7 Max is one of Qwen (Alibaba)'s models. Put "qwen/qwen3.7-max" in the model field and the rest of your code stays as it is. Qwen3.7 Max keeps 1,000,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "mid-range" band — cheaper than 125 and dearer than 272 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 2,656 toman per 1,000-word exchange (510,674 in, 1,532,021 out, per 1M tokens). It supports cached input: repeated context is billed at 102,135 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 GLM 5.1, and Qwen3.7 Max 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 38 thousand-word requests on Qwen3.7 Max, and every 1,000 toman is about 377 words of round trip. A job with one million input tokens and one million output tokens comes to 2,042,694 toman in total. Filling this model's 1,000,000-token window costs 510,674 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3.8-2.4t-a95b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 29% cheaper (2,656 against 3,601 toman). The context windows differ too: 1,000,000 against 1,048,576 tokens. On parameters the other takes logit_bias, min_p, reasoning_effort, 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 logprobs, presence_penalty, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 tokens.

What to use it for: 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.

Provider's own description

Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...

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 out1,379 toman
Summarising a ten-page document4,000 in + 600 out2,962 toman
Classifying a thousand short rows120,000 in + 20,000 out91,921 toman

Frequently asked

How do I call Qwen3.7 Max 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/qwen3.7-max". Nothing else in your code changes.
What does Qwen3.7 Max cost in toman?
510,674 toman per 1M input tokens and 1,532,021 toman per 1M output tokens; a 1,000-word request is around 2,656 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.7 Max take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 131,072 tokens.
Does Qwen3.7 Max 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.