Claude Opus 4.5 is a multimodal model: it can take both text and images in the same request and reason over them jointly. You can send screenshots, charts, scanned documents, UI captures, or camera photos together with a natural-language instruction. The model will visually parse structures like tables, plots, and diagrams, then relate them to your text query. For example, you can give it an image of a financial chart plus a question like, “Explain the revenue trend and comment on the anomaly in Q3,” and it will use both the image and any accompanying text context to answer.
A typical mixed-input workflow looks like this: you upload one or more images (e.g., PNG screenshots or photos of whiteboards), then describe what you want — “extract the table,” “turn this into clean CSV,” “compare this chart to the targets described below,” etc. Opus 4.5 will do OCR-style reading, but also higher-level reasoning (recognizing axes, legends, color-coded series, or layout conventions). For UI or product analytics work, you might feed it both the image of a dashboard and a textual description of business rules, and ask it to propose actions or diagnose issues.
When you also maintain a retrieval layer with a vector database such as Milvus or Zilliz Cloud, you can store embeddings derived from both text and image metadata. For example, you might embed chart captions, dashboard section titles, or alt-text-like annotations and store them in Milvus. When a user drops in a new screenshot, your system can (1) ask Claude Opus 4.5 to extract a textual summary; (2) embed that summary; and (3) query Milvus/Zilliz Cloud for similar dashboards, incidents, or past analyses. That lets you build multimodal “memory” where images and text are tied together in a semantically searchable space.