Nano Banana and Flux are two of the most cost-effective AI image models available right now. Both are significantly cheaper than Midjourney. But they have different strengths, and the right choice depends on what you're generating.
Quick verdict
- Cheapest: Flux 2 Dev or Flux Schnell (1 credit, ~$0.017/image)
- Best photorealism: Nano Banana 2 (3 credits, ~$0.052/image)
- Best balance: Nano Banana base (2 credits, ~$0.035/image)
The models
Nano Banana (Google Imagen)
Nano Banana is Google's Imagen family of text-to-image models, available via JourneyAPI in three variants:
- nano-banana — base model, 2 credits. Fast (~3s), strong general-purpose output.
- nano-banana-2 — 3 credits. State-of-the-art photorealism, better face rendering, multi-subject coherence.
- nano-banana-hd — 3 credits. High-detail variant, optimized for rich textures and fine detail.
All Nano Banana variants support reference images and multiple aspect ratios. Nano Banana 2 additionally supports reference image blending — useful for consistent character or style generation.
Flux (Black Forest Labs)
Flux is a family of open-weight text-to-image models from Black Forest Labs. Two generations are available:
- flux-schnell — 1 credit. Fastest Flux model, optimized for speed. Good for iteration.
- flux-dev — 2 credits. Flux 1 development model. Higher quality than Schnell.
- flux-pro — 4 credits. Flux 1 premium tier.
- flux-2-dev — 1 credit. Flux 2's base model. Better quality than Flux 1 Dev at the same cost-tier.
- flux-2-flex — 2 credits. Balanced quality and speed.
- flux-2-max — 4 credits. Flux 2 highest quality.
Pricing comparison
| Model | Family | Credits | $/image (Hobby) | $/image (Pro) |
|---|---|---|---|---|
| flux-schnell | Flux 1 | 1 | $0.017 | $0.017 |
| flux-2-dev | Flux 2 | 1 | $0.017 | $0.017 |
| nano-banana | Google Imagen | 2 | $0.035 | $0.033 |
| flux-2-flex | Flux 2 | 2 | $0.035 | $0.033 |
| nano-banana-2 | Google Imagen | 3 | $0.052 | $0.050 |
| nano-banana-hd | Google Imagen | 3 | $0.052 | $0.050 |
| flux-2-max | Flux 2 | 4 | $0.069 | $0.066 |
At the 1-credit tier, Flux 2 Dev and Flux Schnell are the cheapest. Nano Banana's base model starts at 2 credits — twice the cost — but delivers noticeably better photorealism. At 3 credits, Nano Banana 2 competes with Flux 2 Max on quality but at a lower price.
Quality differences
Where Nano Banana is stronger
- Photorealism. Nano Banana 2 produces highly realistic images with natural lighting, skin texture, and material detail. Google's training data advantages show here.
- Faces. Better face rendering and multi-subject coherence. More reliable for portraits or images with people.
- Reference images. Nano Banana 2 supports reference blending — useful for consistent output across a batch.
Where Flux is stronger
- Price at the low end. Flux 2 Dev (1 credit) produces quality that outpaces Nano Banana Base (2 credits) in many aesthetic styles, at half the price.
- Artistic and stylized output. Flux tends to handle stylized, illustrative, and non-photorealistic prompts better.
- Speed. Flux Schnell is the fastest model available — good for rapid iteration where you're testing prompts rather than producing finals.
When to use Nano Banana
- You need photorealistic output — product photos, portraits, lifestyle imagery
- Your images include people, and face quality matters
- You're generating at moderate volume where the 2–3 credit cost is acceptable
- You want reference image support for style consistency across a batch
When to use Flux
- You need the lowest possible cost per image — flux-2-dev at 1 credit is hard to beat
- You're prototyping or iterating through prompts (use flux-schnell)
- Your output is stylized, illustrative, or non-photorealistic
- You're generating at high volume and cost per image is the primary constraint
Verdict
For pure cost efficiency at scale: flux-2-dev. One credit per image, good quality, no competition at that price point.
For photorealistic output where quality is non-negotiable: nano-banana-2. The step up to 3 credits is worth it if images are customer-facing and realism matters.
The best approach is to use both: route prototyping and non-critical generations to flux-2-dev, and high-stakes output to nano-banana-2. All via the same /imagine endpoint — just change the model field.