Vision and Multimodal

Vision and Multimodal models can understand both text and images. They provide capabilities through a unified OpenAI-compatible Chat Completions API and are suitable for vision-language tasks such as image description, visual Q&A, and text-image workflows. Representative models include:

Qwen/Qwen2.5-VL-72B-Instruct
google/gemma-4-31B-it

For the full model list and pricing, refer to Models.

Core Capabilities

Image understanding: Describe images, extract text (OCR), and answer questions about visual content.
Visual Q&A: Ask follow-up questions about diagrams, screenshots, or product images.
Multimodal conversation: Mix text and images in a single conversation, such as “compare these two charts.”
Content moderation: Classify or flag image content using natural language instructions.

Image Message Format

When sending images, set content in messages to an array. Array items can be of type text or image_url. The image_url can be either a publicly accessible image URL or a data: URL, which is a base64-encoded image. Example structure:

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What is shown in this image?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://images.unsplash.com/photo-1506748686214-e9df14d4d9d0",
                },
            },
        ],
    }
]

Key Parameters

Parameters
- temperature: Controls output randomness.
- max_completion_tokens: Limits generation length and helps avoid truncated output.
- stream=True: Returns responses as a stream. Recommended for long responses to reduce timeout risk.
Context The maximum context length supported by each model can be viewed in Models.
Image limits Supported image formats and size limits vary by model. For details, refer to Models.

Billing

Formula: Total cost = (input tokens x input unit price) + (output tokens x output unit price)
Pricing: The input unit price for vision models, measured by token, may differ from text-only models. Check the model detail page in Models for specific vision input pricing and whether any per-image caps or minimums apply.
Token calculation notes Total input tokens = text tokens + image tokens.
- Images and other vision inputs are converted into input tokens for billing. The pixel-to-token mapping differs by model: higher resolution and more images usually produce more input tokens.
- Text in the same request is still counted in the standard way.
- If there are multiple images, each image is counted separately.

Token calculation examples The following representative models illustrate how visual content is converted into tokens:

Model	Visual tokenization (brief)
`Qwen/Qwen2.5-VL-72B-Instruct`	The image is divided into patches/tiles, and each tile is encoded as tokens. The total number of image tokens depends on the resolution and the maximum size allowed by the model, such as 1280 x 1280 or a similar value.
`google/gemma-4-31B-it`	The vision encoder maps the image to a sequence of tokens based on patches. The sequence length depends on the input resolution and model configuration.

Examples

The examples read the API key from an environment variable to avoid writing secrets into code.

export API_KEY="YOUR_API_KEY"

Image Description

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://api.scitix.ai/model-api",
)

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image in one or two sentences."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://images.unsplash.com/photo-1506748686214-e9df14d4d9d0",
                    },
                },
            ],
        }
    ],
    max_completion_tokens=256,
)

print(response.choices[0].message.content)

Visual Q&A with Base64 Images

import os
import base64
from openai import OpenAI


def image_to_data_url(path: str) -> str:
    with open(path, "rb") as f:
        b64 = base64.standard_b64encode(f.read()).decode()
    return f"data:image/jpeg;base64,{b64}"


client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://api.scitix.ai/model-api",
)

url = image_to_data_url("myphoto.jpeg")

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is the main message or call-to-action on this screen?",
                },
                {"type": "image_url", "image_url": {"url": url}},
            ],
        }
    ],
    max_completion_tokens=512,
)

print(response.choices[0].message.content)

Multiple Images in One Request

To send multiple images in a single request, add more image_url entries to the same content array. The model receives these images together and can use them for tasks such as comparison and summarization.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://api.scitix.ai/model-api",
)

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Compare these two images in one or two sentences. What do they have in common or how do they differ?",
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://images.unsplash.com/photo-1551963831-b3b1ca40c98e",
                    },
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://images.unsplash.com/photo-1472214103451-9374bd1c798e",
                    },
                },
            ],
        }
    ],
    max_completion_tokens=512,
)

print(response.choices[0].message.content)

Get Started

Models

Usage

API Guides

Tool Integrations

Vision and Multimodal

Core Capabilities

Image Message Format

Key Parameters

Billing

Examples

Image Description

Visual Q&A with Base64 Images

Multiple Images in One Request

​Core Capabilities

​Image Message Format

​Key Parameters

​Billing

​Examples

​Image Description

​Visual Q&A with Base64 Images

​Multiple Images in One Request

Core Capabilities

Image Message Format

Key Parameters

Billing

Examples

Image Description

Visual Q&A with Base64 Images

Multiple Images in One Request