Nano Banana vs FLUX vs Seedream: AI Image Quality Test

Three models, three approaches to the same problem: generating high-quality images fast and affordably. Nano Banana prioritizes speed with a novel distillation architecture. FLUX Dev balances quality and cost with an open-weight design. Seedream 3.0 pushes quality ceiling at a moderate price point.

We ran all three through identical test suites — 100 prompts across five categories — and scored every output. Here is what we found.

TL;DR

• Best quality: Seedream 3.0 — highest photorealism and detail scores
• Best speed: Nano Banana — under 3 seconds consistently
• Best value: FLUX Dev — strong quality at the lowest effective cost
• For production work: Seedream 3.0 or FLUX Dev depending on budget
• For real-time/interactive: Nano Banana

Quick Comparison

The Test Methodology

We generated 100 images per model using the same prompts, organized into five categories:

1. Photorealistic portraits (20 prompts)
2. Product photography (20 prompts)
3. Landscapes and environments (20 prompts)
4. Artistic and stylized (20 prompts)
5. Text-heavy designs (20 prompts)

Each image was scored by three evaluators on a 1-10 scale across photorealism, detail, prompt adherence, and aesthetic appeal. Scores were averaged.

Photorealistic Portraits

This is where Seedream 3.0 dominates. Skin texture, pore detail, eye reflections, hair strand rendering — every micro-detail is handled with a level of precision that makes output look like high-end portrait photography.

Seedream 3.0 average score: 9.4/10. Consistent quality across skin tones, ages, and lighting conditions. Particularly strong with complex lighting — rim light, split light, Rembrandt lighting patterns all rendered accurately.

FLUX Dev average score: 8.1/10. Solid photorealism that holds up well at normal viewing sizes. Fine detail is slightly less refined than Seedream, particularly in hair rendering and eye detail. Still very usable for commercial applications.

Nano Banana average score: 7.8/10. Impressive given the speed. Portraits are clean and natural but lack the micro-detail of the other two models. Skin can appear slightly smoothed, and hair rendering is more simplified. Excellent for social media thumbnails and web content.

Product Photography

Product shots demand accuracy — correct proportions, realistic materials, proper lighting on surfaces. This category revealed the biggest quality gaps between models.

Seedream 3.0 average score: 9.2/10. Metal surfaces reflect correctly. Glass is transparent with proper refraction. Fabric textures are detailed and accurate. Products look ready for a catalog.

FLUX Dev average score: 8.3/10. Strong product rendering with occasional issues on highly reflective surfaces. Chrome and glass are good but not perfect. Matte surfaces and fabric are handled well.

Nano Banana average score: 7.5/10. Acceptable for web product listings but not for print or high-resolution applications. Material rendering is simplified — metal looks like metal but without the nuanced reflections of the other models.

Landscapes and Environments

All three models perform well on landscapes. The quality gap narrows because landscapes are less sensitive to fine detail — viewers evaluate atmosphere and composition rather than pixel-level accuracy.

Artistic and Stylized Output

Stylized prompts — watercolor, oil painting, pixel art, anime — level the playing field. When photorealism is not the goal, the quality differences between models shrink.

Seedream 3.0 average score: 8.6/10. Adapts style well but occasionally retains too much photorealistic detail in stylized outputs, creating a hybrid look that is not always intentional.

FLUX Dev average score: 8.0/10. Clean style adaptation with the benefit of community fine-tunes. Want anime-specific quality? There is a FLUX LoRA for that. This extensibility gives FLUX Dev an effective advantage in niche styles.

Nano Banana average score: 7.6/10. Handles major styles competently. Subtle style prompts — "in the style of Moebius" or "volumetric watercolor" — are interpreted more generically than the other models.

Text Rendering

The most variable category. Text in images remains challenging for all AI models, but the gap between best and worst is significant.

Seedream 3.0 average score: 8.8/10. Short text (1-5 words) renders correctly in most cases. Longer text degrades. Font style control is good — "bold sans-serif" and "elegant script" produce visibly different results.

FLUX Dev average score: 7.9/10. Competent text rendering for headlines and short labels. Occasional letter substitutions in longer text. Good enough for mockups that will have text replaced in post.

Nano Banana average score: 6.5/10. Text rendering is the weakest area. Short brand names sometimes render correctly, but anything beyond 3-4 words is unreliable. Not recommended for text-dependent outputs.

Speed in Practice

Raw generation time tells only part of the story. Effective speed includes time to iterate and reach a usable result.

Nano Banana's raw speed advantage is partially offset by a lower first-attempt success rate. Seedream's higher hit rate means fewer re-generations, making its effective speed competitive despite a slower per-image time.

Cost Efficiency Analysis

For a team generating 500 images per month:

The price difference between Nano Banana and Seedream 3.0 is $15 per month for 500 images. For most teams, the quality uplift justifies the cost. For high-volume operations generating thousands of images, Nano Banana's savings become more meaningful.

Which Model Should You Choose?

Seedream 3.0 if quality is your priority. Best photorealism, best text rendering, highest consistency. All three cost the same $0.10 per image, so picking the strongest one costs you nothing extra.

FLUX Dev if you want the best balance of quality and flexibility. Open weights mean community fine-tunes for every niche. API access enables automation. The quality floor is high enough for production work.

Nano Banana if speed is the dominant constraint. Real-time applications, high-volume draft generation, interactive tools where sub-3-second response matters — this is Nano Banana's territory.

The Bottom Line

All three models are good enough for professional use. The differences are real but contextual — what matters most depends on your output medium, your volume, and your quality threshold. Test all three with your actual prompts before committing. The results may surprise you.