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Multimodal Input

Vision-capable chat models accept images inside a user message. The content becomes an array of typed parts instead of a single string. This guide covers the two ways to attach an image — by URL or as a base64 data URI — and how to mix text and images in a single turn.

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Two ways to attach an image

Public URL

Reference an image that the gateway can fetch over HTTPS. Best for static assets, CDN-hosted images, and pre-signed URLs.

Base64 data URI

Inline the image bytes as a base64 string. Best for ephemeral content, user uploads, and private assets.
Prefer URLs when possible. The gateway can cache and reuse them across requests. Base64 is best reserved for images that change per request.

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URL example

Pass the image as a content part with type: image_url: 
curl https://api.getinfinityblue.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-5.4","messages":[{"role":"user","content":[{"type":"text","text":"What is in this image?"},{"type":"image_url","image_url":{"url":"https://example.com/cat.jpg"}}]}]}'
The URL must be reachable from the gateway. If your bucket is private, generate a short-lived pre-signed URL or switch to base64.

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Base64 example

Inline the bytes as a data URI: 
import base64
from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.getinfinityblue.com/v1")
encoded = base64.b64encode(open("cat.jpg", "rb").read()).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What is in this image?"},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded}", "detail": "high"}},
        ],
    }],
)
print(response.choices[0].message.content)
The detail parameter controls how much compute the model spends on the image. low is faster and cheaper; high reads small text and dense details; auto lets the model pick.

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Multiple images in one turn

Add as many content parts as you need. The model reads them in order: 
{
  "model": "gpt-5.4",
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "Compare these two receipts and tell me which has the larger tip."},
        {"type": "image_url", "image_url": {"url": "https://example.com/receipt-a.jpg"}},
        {"type": "image_url", "image_url": {"url": "https://example.com/receipt-b.jpg"}}
      ]
    }
  ]
}

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Limits and gotchas

  • Each model has a maximum number of images per request and a maximum resolution. Excess is rejected with 400 invalid_request_error.
  • Total request size — text plus encoded images — must stay under the per-request byte cap. For base64, encode the image first and check len(b64) * 3 / 4 against the limit.
  • When you mix text and images, keep the text part concise. The model reads both, but verbose prompts dilute the focus on the visual.

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Streaming and tools

Vision works with streaming and with tool calls. The same content array works in stream: true requests and in requests that include tools. The model emits text and tool deltas the same way it does for a text-only message, with the image already understood as part of the context.
Never put sensitive images in a public URL. If the asset is private, use base64 or a pre-signed URL with a short expiration.