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

OpenRouter’s vision guide explains how to send images to vision-capable large language models through its Chat Completions API by using a user message content array containing text and an `image_url` element, which can reference either a public hosted image or a base64-encoded local or private file. The same request format works across supported multimodal models, allowing developers to switch models by changing only the model identifier while comparing trade-offs in cost, latency, context size, OCR quality, chart interpretation, and general image understanding. Images can be submitted in groups for comparisons or multi-page documents, although image count, resolution, and associated token costs depend on the provider and model, making downscaling and cropping useful for efficiency and accuracy. The guide describes image processing as conversion of visual patches into embeddings treated as tokens, so larger images consume more tokens. It also outlines multimodal retrieval-augmented generation approaches that either summarize images into searchable text or use native multimodal embeddings, then combine retrieved text and images in a final model request. This method is presented as unsuitable for real-time video, pixel-perfect small-text OCR without preprocessing or dedicated tools, and image generation or editing, while structured extraction can be supported through tool calling or structured outputs.

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