I tested OpenAI's new image model against Google's latest and the results surprised me

Two massive AI image generators dropped within days of each other, and honestly? I wasn't expecting such different approaches to the same problem.

OpenAI launched GPT Image 1.5 with promises of being "up to 4x faster" than before. Google countered with Nano Banana Pro, claiming it can handle up to 14 reference images at once. But you know how marketing works - the real question is what happens when you actually try to use these things for real creative work.

I spent the last week putting both through their paces, and the differences are more interesting than I expected.

What makes these models different

Before diving into testing, let me break down what each model is actually trying to do.

GPT Image 1.5 is OpenAI's speed play. They've focused on making image generation faster and more efficient. The idea is simple: if you can iterate quickly, you can get better results. No more waiting 30 seconds for each attempt when you're trying to nail that perfect product shot.

Nano Banana Pro takes a completely different approach. Google's betting on visual context - the ability to feed the model multiple reference images and have it understand relationships between them. Think mood boards, style references, composition guides all rolled into one prompt.

It's like comparing a sports car to a Swiss Army knife. Both useful, but for different reasons.

Speed testing: Does faster actually matter?

I'll be honest - I was skeptical about the 4x speed claim. These marketing numbers rarely translate to real-world improvements.

But GPT Image 1.5 genuinely surprised me. Simple prompts that used to take 25-30 seconds now finish in about 8-10 seconds. Complex scenes with multiple objects? Still under 20 seconds most of the time.

Here's why this matters more than you might think: when you're iterating on creative work, speed compounds. If you're doing product photography and need to test 5 different angles, that's the difference between 2 minutes and 8 minutes of waiting. Over a full project, those minutes add up to hours.

Nano Banana Pro is noticeably slower, especially when you're using multiple reference images. A prompt with 8-10 reference images can take 45 seconds to a minute. That's not terrible, but it definitely changes how you work with it.

The reference image advantage

This is where Nano Banana Pro starts to shine. Being able to upload 14 reference images sounds gimmicky until you try it for something like brand consistency.

I tested this with a skincare product shoot. I uploaded reference images showing:

The specific marble texture I wanted for the pedestal
Eucalyptus leaves for background styling
Lighting reference from a similar product
Color palette examples
Composition layouts

The result? Nano Banana Pro nailed it. The composition followed my references perfectly - serum on the left, cream centered on the marble pedestal, face mask on the right, eucalyptus filling the background naturally with soft lighting from the left creating realistic shadows.

GPT Image 1.5, working from just text descriptions, gave me something that looked professional but generic. It hit the basic requirements but missed the nuanced styling that makes product photography actually sell.

Where each model excels

After extensive testing, here's what I've found each model does best:

GPT Image 1.5 strengths:

Speed for iteration: Perfect when you need to test lots of variations quickly
Clean execution: Rarely produces weird artifacts or obvious mistakes
Text integration: Better at incorporating text elements into designs
Consistency: Results tend to be predictable and reliable

Nano Banana Pro strengths:

Visual understanding: Exceptional at interpreting reference images and mood boards
Search grounding: Can pull in real-world data for infographics and educational content
Complex compositions: Handles multi-element scenes with better spatial awareness
Style transfer: Excellent at matching specific artistic styles from references

The search grounding game-changer

One feature that really caught my attention is Nano Banana Pro's search grounding capability. This lets it access current information for creating infographics, charts, and educational content.

I asked it to create an infographic showing the five largest economies by GDP. Instead of relying on potentially outdated training data, it pulled current information and created an accurate, well-designed infographic with proper proportions and recent numbers.

GPT Image 1.5 can't do this. It's limited to its training data, which might be months or years old. For any content that needs current accuracy - like data visualization, educational materials, or news-related graphics - this is a significant advantage.

Real-world workflow differences

Using these tools daily reveals some interesting workflow patterns:

With GPT Image 1.5, I find myself doing rapid-fire iterations. The speed makes it easy to try multiple approaches quickly. I'll often generate 8-10 variations in the time it used to take for 2-3. This works great for exploring ideas or when you need volume.

Nano Banana Pro encourages more thoughtful preparation. You spend time gathering references, thinking through your visual approach, then get results that are usually closer to your vision on the first try. It's more like working with a skilled designer who needs a detailed brief.

The surprising detail differences

Something subtle but important: both models handle object removal and editing differently. When I asked each to remove a distracting element from a product photo while keeping everything else intact, GPT Image 1.5 executed cleanly without any artifacts.

Nano Banana Pro also removed the object successfully and kept the overall composition, but made subtle changes to facial details and textures. Not necessarily worse, but different. It seems to interpret "editing" as an opportunity to refine the overall image, not just make the requested change.

Cost and accessibility considerations

Neither model is free to use extensively, but their pricing models reflect their different approaches.

GPT Image 1.5's faster generation means you burn through credits more quickly if you're not careful, but you also get results faster. There's a psychological element here - the quick feedback loop makes it easier to get into a flow state.

Nano Banana Pro's credit usage scales with complexity. Simple prompts with one reference image cost about the same as other models, but those 14-image reference prompts can get expensive fast.

Both offer sharing features where you can browse community creations and even get token rewards if people download your work, which is a nice touch for offsetting costs.

Which should you choose?

It really depends on how you work and what you're creating.

Choose GPT Image 1.5 if you:

Need to iterate quickly on ideas
Work on projects with tight deadlines
Prefer clean, predictable results
Do a lot of text-heavy design work

Choose Nano Banana Pro if you:

Have specific visual references to match
Create educational or data-driven content
Work on projects where style consistency matters
Don't mind spending more time on setup for better initial results

Honestly, for serious creative work, having access to both makes the most sense. I find myself using GPT Image 1.5 for exploration and quick iterations, then switching to Nano Banana Pro when I know exactly what I want and have the references to communicate it clearly.

The AI image generation space is moving fast, and these two models represent genuinely different philosophies about how creative AI should work. Neither is obviously better - they're tools for different parts of the creative process.

What's exciting is that we're finally getting past the "can AI generate decent images?" phase and into "which AI approach works best for your specific creative workflow?" That's a much more interesting question.