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Qwen3.7 Plus (batch) API: toman pricing and code

qwen/qwen3.7-plus:batch

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
79,630 toman
Output · per 1M tokens
318,522 toman
One 1,000-word request ≈
518 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Qwen3.7 Plus (batch) example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.7-plus:batch",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="qwen/qwen3.7-plus:batch",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "qwen/qwen3.7-plus:batch",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Qwen3.7 Plus (batch) good for?

Qwen (Alibaba) publishes this model; we expose it under the id "qwen/qwen3.7-plus:batch". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 261 and dearer than 137 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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 518 toman per 1,000-word exchange (79,630 in, 318,522 out, per 1M tokens). 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 4.6V, and Qwen3.7 Plus (batch) 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 193 thousand-word requests on Qwen3.7 Plus (batch), and every 1,000 toman is about 1,931 words of round trip. A job with one million input tokens and one million output tokens comes to 398,152 toman in total. Filling this model's 262,144-token window costs 20,875 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "qwen/qwen3.7-plus", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out about 29% cheaper (518 against 720 toman). The context windows differ too: 262,144 against 1,000,000 tokens.

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 Aion 3.5 at 262,144 tokens.

What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, 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-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...

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 out247 toman
Summarising a ten-page document4,000 in + 600 out510 toman
Classifying a thousand short rows120,000 in + 20,000 out15,926 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the batch variant.

VariantModel idOutput / 1MContext
standardqwen/qwen3.7-plus443,1611,000,000

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Qwen3.7 Plus (batch) 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-plus:batch". Nothing else in your code changes.
What does Qwen3.7 Plus (batch) cost in toman?
79,630 toman per 1M input tokens and 318,522 toman per 1M output tokens; a 1,000-word request is around 518 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3.7 Plus (batch) take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 131,072 tokens.
Does Qwen3.7 Plus (batch) support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.