AI for Images and Screenshots: How to Choose Between GPT-5.4, Claude, and Gemini

Which AI model should read your images and screenshots?

Pick by the visual task, not by a single best model. For dense charts, diagrams, and on-screen layouts, Gemini 3.1 Pro is the natural first pick: Google trained it as a native multimodal model, so images, text, audio, and video share one pipeline, and it is documented as strong on document, chart, and screen understanding. For screenshots and anything where the model has to read a user interface (buttons, menus, click targets), GPT-5.4 is built for the job: OpenAI designed it to operate computers from screenshots, and its general visual perception improved alongside that. For long documents made of many pages, or when you want a model to weigh several images at once, Claude Opus 4.8 is a steady choice: it accepts high-resolution images and analyzes multiple images jointly in one request.

The honest part: the gaps are small and task-dependent, so the reliable move is to test your own image on more than one model rather than trust a leaderboard.

What can GPT-5.4, Claude Opus 4.8, and Gemini 3.1 Pro do with images?

All three accept image input and return text. The differences are in emphasis.

• Gemini 3.1 Pro is native multimodal (text, images, audio, and video trained in from the ground up). Google DeepMind documents strength on document analysis, chart interpretation, technical diagrams, and screen and spatial understanding, and it preserves an image's native aspect ratio when it reads it.
• GPT-5.4 pairs strong general visual perception with native computer-use: it reads screenshots and issues mouse and keyboard actions, and OpenAI added an "original" image-detail setting for full-fidelity perception of large images, plus improved document parsing.
• Claude Opus 4.8 accepts text and image input with a large context window, takes high-resolution images, and analyzes several images together in a single request, which suits pages of a scanned document (per Anthropic).

DeepSeek V3.2 is mainly a text and cost play. If a chat needs to read an image, put a vision-capable model on it.

When does each model win?

Treat the table as a starting bias, not a verdict. If the cost of a misread is high, read the image twice.

How do I compare image readings across models in aiDex?

Drop the image once and every model in the chat reads the same file. Open aiDex, pick two or three vision models from the Dex, and use Compare to line up their readings side by side, the way you would when reading a chart or spreadsheet. When the readings disagree (a number on a chart, a label on a diagram, text in a screenshot), switch to Judge and let one model check the others against the image itself. For a recurring job, like a weekly dashboard, save the lineup as a Teams chat. Use your own provider keys or the ones we manage, and pick the models you want.

Do you always need a multimodal model?

No. If you can describe the content in plain text, a text prompt is cheaper and often enough. Reach for vision when the layout carries the meaning: a chart you cannot copy as numbers, a screenshot of an error, a scanned contract, a wiring diagram. When the read has to be right, read the image with two models and reconcile, instead of trusting one pass. This is the same habit behind reliable multi-model AI workflows and behind picking which model fits which task.