How do I call Ling 3.0 Flash VL 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 "inclusionai/ling-3.0-flash-vl". Nothing else in your code changes.

What does Ling 3.0 Flash VL cost in toman?

20,773 toman per 1M input tokens and 62,319 toman per 1M output tokens; a 1,000-word request is around 108 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Ling 3.0 Flash VL take?

Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 32,768 tokens.

Does Ling 3.0 Flash VL 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.

Ling 3.0 Flash VL API: toman pricing and code

inclusionai/ling-3.0-flash-vl

Image inputTool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

20,773 toman

Output · per 1M tokens

62,319 toman

One 1,000-word request ≈

108 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 4,155 toman per 1M tokens.Pricing and top-ups

Ling 3.0 Flash VL 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.

curlPythonNode.js

# 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": "inclusionai/ling-3.0-flash-vl",
    "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="inclusionai/ling-3.0-flash-vl",
    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: "inclusionai/ling-3.0-flash-vl",
  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 Ling 3.0 Flash VL good for?

The id for Ling 3.0 Flash VL in our API is "inclusionai/ling-3.0-flash-vl", served from inclusionAI. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 32,768 tokens. On price it sits in the "very cheap" band — cheaper than 365 and dearer than 31 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 20,773 toman per 1M input tokens and 62,319 per 1M output tokens. A 1,000-word round trip on Ling 3.0 Flash VL lands near 108 toman. It supports cached input: repeated context is billed at 4,155 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 5.3 Flash (batch), and Ling 3.0 Flash VL costs about the same, 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 926 thousand-word requests on Ling 3.0 Flash VL, and every 1,000 toman is about 9,259 words of round trip. A job with one million input tokens and one million output tokens comes to 83,093 toman in total. Filling this model's 131,072-token window costs 2,723 toman on the input side alone, which is the real reason to keep conversation history short.

inclusionAI has 4 models in our catalogue; the cheapest is Ling 3.0 Tiny at 27 toman per thousand words and the dearest Ling 3.1 Flash at 135. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion-2.0 at 131,072 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.

Provider's own description

Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual...

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	56 toman
Summarising a ten-page document	4,000 in + 600 out	120 toman
Classifying a thousand short rows	120,000 in + 20,000 out	3,739 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

56 toman

Summarising a ten-page document

4,000 in + 600 out

120 toman

Classifying a thousand short rows

120,000 in + 20,000 out

3,739 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
13 Oct 2026	20,773	62,319
25 Sep 2026	7,271	21,327
11 Sep 2026	20,773	62,319

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Ling 3.0 Flash VL 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 "inclusionai/ling-3.0-flash-vl". Nothing else in your code changes.
What does Ling 3.0 Flash VL cost in toman?: 20,773 toman per 1M input tokens and 62,319 toman per 1M output tokens; a 1,000-word request is around 108 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ling 3.0 Flash VL take?: Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 32,768 tokens.
Does Ling 3.0 Flash VL 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.