Seedream 5.0 Lite
by ByteDance
ByteDance's image model that reads the prompt carefully and pulls in live web context, for news‑aware and topical visuals.
Seedream 5.0 Lite
by ByteDance
Key features
Technical specifications
5.0 Lite
Lite variant of Seedream 5.0
Deep thinking
Plans before generating
Online search
Live web information at generation time
Prompt-following
Tested on ByteDance's MagicBench
Use cases
Topical and news-aware visuals
Generate images tied to current events, fresh launches, or trending references. Online search context bypasses the training cutoff.
Reasoning-heavy prompts
Prompts that need careful reading before drawing. Multi-element scenes, layered instructions, or anything where older models drop detail.
Typography and UI design
Generate posters, UI mockups, and chart-style visuals where typography and structured layout need to render coherently inside the image.
Editorial and photography
Standard editorial and photography prompts benefit from the same deep thinking. Each prompt is read carefully before pixels are committed.
Prompt examples
Topical visual
Editorial illustration of a recently launched flagship device on a designer desk, top-down flat lay, accurate to current real-world references
Edit promptReasoning-heavy
A bookshop window in autumn, hand-lettered chalkboard sign with the word "new arrivals" in clean script, warm tungsten light, photoreal
Edit promptTypography-led
Vintage travel poster for Lisbon, large condensed title across the top, small descriptive copy along the bottom, warm muted palette
Edit promptOverview
Seedream 5.0 Lite is ByteDance's unified multimodal image generation model, and the most distinct shift in the Seedream line. Two capabilities define it: deep-thinking reasoning before generation, the model interprets the prompt carefully before committing to an image, and built-in online search, so prompts tied to current events or fresh references can be visualized with live web context rather than being limited to training-cutoff knowledge. On Morphic, both behaviors live inside a single model selection in Copilot, no separate research or context-injection step needed.
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