Seedream 4 vs Nano Banana: historical test and current successors

What replaced Seedream 4 and the original Nano Banana?

ByteDance describes Seedream 5.0 Pro as its current professional multimodal image model for high-density information, interactive editing and multilingual generation. Its own launch note acknowledges that fine-grained text rendering and pixel-level editing consistency can still improve. Seedream 5.0 Lite is the lighter reasoning-oriented route with online search capabilities.

Google's current Nano Banana documentation separates four model IDs. Nano Banana 2 is Gemini 3.1 Flash Image, Nano Banana 2 Lite is Gemini 3.1 Flash Lite Image, Nano Banana Pro is Gemini 3 Pro Image, and the original Nano Banana is Gemini 2.5 Flash Image. Google recommends moving from the original model to Nano Banana 2 Lite for newer quality, speed and pricing.

Which current model should you test?

• Test Seedream 5.0 Pro for dense infographics, spatially annotated edits, multilingual layouts and complex production briefs.
• Test Seedream 5.0 Lite when reasoning and current information matter more than maximum production control.
• Test Nano Banana 2 for a general balance of generation, editing, multiple references, text rendering and 4K output.
• Test Nano Banana Pro for complex brand consistency, localization and precision work where Google's premium model is justified.
• Test Nano Banana 2 Lite when throughput, latency and cost matter more than multiple references or long multi-turn edits.

These are provider-described use cases, not an independent ranking. The right choice depends on the exact prompt, provider route, price, reference inputs, editing behavior and acceptance criteria. Use the current Nano Banana alternatives guide and AI image editor comparison for broader shortlists.

Seedream 5 Pro

Nano Banana 2

What the archived Seedream 4 test can and cannot show

The original post retained eight uploaded assets across five prompts. It did not retain a complete reproducibility log: provider, model ID, settings, seed, generation date, latency, cost and both models' outputs are missing for several tasks. We therefore preserve the media as dated examples and describe only what is visible. We do not reuse the old universal winner claims, benchmark score or case-study results.

Archived task 1: annotated room edit

Archived input:

Seedream 4 (archived)

Nano Banana (archived)

This is the only task in the archive that visibly preserves both labeled outputs. A useful review would score placement, preservation of unedited regions, lighting, texture and whether the requested objects are complete. The archive still lacks model settings and generation metadata, so the images should not be generalized to current successor models.

Archived task 2: dense desk prompt

The prompt requested a desk scene with code, a labeled mug, a Venn diagram, a mind map, a phone notification, plants and directional sunlight. The archive preserves one output:

Without a second labeled output or run metadata, this example can illustrate one result but cannot support a model-versus-model conclusion.

Archived task 3: outfit transfer

The prompt asked the model to dress a character from one image in an outfit from another. The archive preserves this input composite and one output:

Review pose, identity, garment shape, texture and background preservation. Do not attribute the result to a current model without rerunning the task.

Archived task 4: seven labeled wallpapers

The prompt requested seven nature wallpapers labeled for Monday through Sunday. The archive preserves one output:

Score all seven labels, count missing or malformed text, and inspect consistency across the set. One selected output does not establish repeatability.

Archived task 5: equation rendering

The prompt requested a blackboard showing two linear equations and their solution steps. The archive preserves one output:

Verify every equation and transformation manually. A visually convincing formula can still be mathematically wrong.

How to rerun the comparison now

• Choose the exact current model IDs and provider routes before generating.
• Use the same source files, prompt, aspect ratio, output size and number of attempts.
• Preserve every output, including failures and rejected attempts.
• Score instruction adherence, subject preservation, text accuracy, edit locality, visual quality and correction effort separately.
• Record latency and total billed cost, then calculate cost per accepted image.
• Publish the generation date and distinguish observed results from provider claims.

For current model breadth and pricing routes, see the best AI image generators guide. You can also run a representative brief through Magic Hour's AI image generator and record the selected model shown in the interface.