How to Send an Image to an LLM via API (Vision Guide)

If you want a vision-capable Large Language Model (LLM) to read a screenshot, inspect a chart, or answer questions about a photo, you need to build the request body correctly. This guide covers image input only, meaning a model reading a picture you give it, not generating or editing one. The pattern is simple. Send a chat message whose content array includes a text part and an image_url part to our image understanding endpoint. After that, the request shape stays the same, and you change only the model field to use any vision-capable model we support. One model handles Optical Character Recognition (OCR) better, another reads UI screenshots more reliably, another reasons over charts more carefully. Because the input shape doesn’t change, you can test those differences without changing your integration. This guide covers sending images to multimodal models, choosing between public URLs and base64 uploads, building multimodal RAG pipelines, and the practical limits that matter once you’re in production. We support multimodal image understanding through the Chat Completions API. Body: a messages array where the user message’s content is a list of parts, one {"type": "text"} and one {"type": "image_url"}. Model: any slug with image input (e.g. anthropic/claude-opus-4.8, google/gemini-3-flash-preview).…