Qwen3.7 Flash API: toman pricing and code
qwen/qwen3.7-flash
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 1,741 toman / 1M.Pricing and top-ups
Qwen3.7 Flash 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-flash",
"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-flash",
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-flash",
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 Flash good for?
Qwen3.7 Flash is one of Qwen (Alibaba)'s models. Put "qwen/qwen3.7-flash" in the model field and the rest of your code stays as it is. Qwen3.7 Flash 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 65,536 tokens. On price it sits in the "very cheap" band — cheaper than 15 and dearer than 391 of the other paid models in the catalogue.
What it can do beyond plain text: 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.
Pricing is 8,704 toman per 1M input tokens and 37,716 per 1M output tokens. A 1,000-word round trip on Qwen3.7 Flash lands near 60 toman. It supports cached input: repeated context is billed at 1,741 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 Gemma 3 12B: Qwen3.7 Flash 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 1,667 thousand-word requests on Qwen3.7 Flash, and every 1,000 toman is about 16,667 words of round trip. A job with one million input tokens and one million output tokens comes to 46,420 toman in total. Filling this model's 1,000,000-token window costs 8,704 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "qwen/qwen3.5-flash-02-23", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 2.1× cheaper (60 against 123 toman). On parameters, this one takes logprobs, top_logprobs while the other takes frequency_penalty, stop, structured_outputs, top_k.
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_logprobs — all through the standard request body. It does not support 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, 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.
Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world...
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 | 28 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 57 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 1,799 toman |
Frequently asked
- How do I call Qwen3.7 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/qwen3.7-flash". Nothing else in your code changes.
- What does Qwen3.7 Flash cost in toman?
- 8,704 toman per 1M input tokens and 37,716 toman per 1M output tokens; a 1,000-word request is around 60 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Qwen3.7 Flash take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,536 tokens.
- Does Qwen3.7 Flash 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.