sgl-project--sglang/docs_new/docs/basic_usage/openai_api_vision.mdx

---
title: "OpenAI APIs - Vision"
metatags:
    description: "This tutorial covers the vision APIs for vision language models."
---
SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.
A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).
This tutorial covers the vision APIs for vision language models.

SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](../supported-models/multimodal_language_models).

As an alternative to the OpenAI API, you can also use the [SGLang offline engine](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py).

## Launch A Server

Launch the server in your terminal and wait for it to initialize.

```python Example
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

example_image_url = "https://raw.githubusercontent.com/sgl-project/sglang/main/examples/assets/example_image.png"
logo_image_url = (
    "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
)

vision_process, port = launch_server_cmd("""
python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-VL-7B-Instruct --log-level warning
""")

wait_for_server(f"http://localhost:{port}", process=vision_process)
```

## Using cURL

Once the server is up, you can send test requests using curl or requests.

```python Example
import subprocess

curl_command = f"""
curl -s http://localhost:{port}/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -d '{{
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "messages": [
      {{
        "role": "user",
        "content": [
          {{
            "type": "text",
            "text": "What’s in this image?"
          }},
          {{
            "type": "image_url",
            "image_url": {{
              "url": "{example_image_url}"
            }}
          }}
        ]
      }}
    ],
    "max_tokens": 300
  }}'
"""

response = subprocess.check_output(curl_command, shell=True).decode()
print_highlight(response)


response = subprocess.check_output(curl_command, shell=True).decode()
print_highlight(response)
```

## Using Python Requests

```python Example
import requests

url = f"http://localhost:{port}/v1/chat/completions"

data = {
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What’s in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": example_image_url},
                },
            ],
        }
    ],
    "max_tokens": 300,
}

response = requests.post(url, json=data)
print_highlight(response.text)
```

## Using OpenAI Python Client

```python Example
from openai import OpenAI

client = OpenAI(base_url=f"http://localhost:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-7B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is in this image?",
                },
                {
                    "type": "image_url",
                    "image_url": {"url": example_image_url},
                },
            ],
        }
    ],
    max_tokens=300,
)

print_highlight(response.choices[0].message.content)
```

## Multiple-Image Inputs

The server also supports multiple images and interleaved text and images if the model supports it.

```python Example
from openai import OpenAI

client = OpenAI(base_url=f"http://localhost:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-7B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": example_image_url,
                    },
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": logo_image_url,
                    },
                },
                {
                    "type": "text",
                    "text": "I have two very different images. They are not related at all. "
                    "Please describe the first image in one sentence, and then describe the second image in another sentence.",
                },
            ],
        }
    ],
    temperature=0,
)

print_highlight(response.choices[0].message.content)
```

```python Example
terminate_process(vision_process)
```