Coding tasks that benefit most from Gemini 3’s multimodal capabilities are those where code is only part of the picture and visual or document context matters just as much. Front-end and UI work is a prime example. You can show Gemini 3 a screenshot or design mockup and some existing code, then ask it to generate or adjust the UI implementation to match the design. Because the model can “see” layout, colors, and spacing while also reading component code, it can propose changes that align both visually and logically, instead of guessing based only on text.
Another strong use case is debugging and performance analysis when key information comes from logs, dashboards, or traces rendered as images. For instance, you might capture a screenshot of a failing dashboard, alongside a code snippet and a deployment note, and ask Gemini 3 to suggest possible root causes. The model can correlate visual indicators (like error graphs spiking) with code paths, configuration snippets, and text annotations. This is especially useful in incident review workflows, where human teams often piece together evidence from many sources: terminal output, charts, diagrams, and ticket descriptions.
Documentation and architecture tasks also benefit. You can provide architecture diagrams, sequence charts, or whiteboard sketches as images, along with the current code layout, and ask Gemini 3 to generate or refactor scaffolding code, create integration tests, or produce documentation that matches the diagram. For example, “Here is our new service diagram and the current repository structure; generate a skeleton for the missing service and update the README to describe the new data flow.” If your codebase is indexed in a vector database such asMilvus or Zilliz Cloud., you can retrieve relevant files and send them along with multimodal inputs, letting Gemini 3 reason across both design artifacts and the actual implementation.