Qwen3.5-9B (batch) API: toman pricing and code
qwen/qwen3.5-9b:batch
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Qwen3.5-9B (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/qwen3.5-9b: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/qwen3.5-9b: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/qwen3.5-9b: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 Qwen3.5-9B (batch) good for?
The id for Qwen3.5-9B (batch) in our API is "qwen/qwen3.5-9b:batch", served from Qwen (Alibaba). Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 235,929 tokens. On price it sits in the "very cheap" band — cheaper than 51 and dearer than 355 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 158 toman per 1,000-word exchange (49,321 in, 72,531 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 5.3 Flash: Qwen3.5-9B (batch) works out roughly 1.3× more expensive, and its context window is smaller. 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 633 thousand-word requests on Qwen3.5-9B (batch), and every 1,000 toman is about 6,329 words of round trip. A job with one million input tokens and one million output tokens comes to 121,853 toman in total. Filling this model's 262,144-token window costs 12,929 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "qwen/qwen3.5-9b", 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 1.7× dearer (158 against 94 toman). On parameters the other takes logprobs, seed, top_logprobs.
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 logit_bias, min_p, repetition_penalty, top_k — all through the standard request body. It does not support seed, 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 Trinity Large Thinking 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.
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
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 | 103 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 241 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 7,369 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | qwen/qwen3.5-9b | 43,519 | 262,144 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Qwen3.5-9B (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/qwen3.5-9b:batch". Nothing else in your code changes.
- What does Qwen3.5-9B (batch) cost in toman?
- 49,321 toman per 1M input tokens and 72,531 toman per 1M output tokens; a 1,000-word request is around 158 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Qwen3.5-9B (batch) take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
- Does Qwen3.5-9B (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.