After actually using AI images in content creation, you will discover a very realistic issue: the first image is usually not the final version.
The character direction may be wrong, the scene may be too empty, the color palette may be too vivid, the protagonist may not stand out enough, or the image may be beautiful but not quite consistent with what the article wants to express. Especially in article illustrations, video storyboards, product concept images, and social media content, creators often need to generate several versions first, then choose one direction to continue refining.
At this point, what affects the experience is not only whether the model can generate a good image, but also: how long it takes to generate an image, whether the cost of each revision is high, whether complex Prompts can be understood, and whether it can be integrated into existing API workflows.
This is also the thing we most wanted to confirm when testing Nano Banana 2.1: can it find a comfortable balance between speed, visual expression, and cost of use?
¶ We tested it with a “not very friendly” Prompt
To avoid testing only simple portraits or landscapes, we designed an Eastern wuxia and xianxia scene with a large amount of information. The image includes a suspended sword sect in a torrential rainstorm, a sword-wielding protagonist, a female character controlling multi-layer formations, an old warrior monk guarding the mountain gate, disciples escorting children during evacuation, masked enemies in the sky, as well as a golden heavenly gate, a galaxy, and an ancient dragon silhouette.
The Prompt also specified a low-angle shot, a 28mm cinematic lens, foreground-middle-background relationships, cool-warm color contrast, and structural requirements such as hands, weapons, and character relationships.
The difficulty of this type of Prompt is not generating a beautiful character, but requiring the model to handle characters, environments, actions, lighting, and narrative relationships at the same time.
¶ Nano Banana 2.1 generation results
The configuration for this test was nano-banana-2.1, 16:9, 1K, single-image generation.
The actual generation time was approximately 44.44 seconds, and the task cost was 0.20 credit. The final image size was 1376×768, the output format was JPEG, and the file size was approximately 0.903 MiB.
At first glance, this image does not blindly pursue cramming in every element. The male sword cultivator is the clear visual center, while the female formation master, the old gatekeeper, and the evacuating crowd are distributed across different areas. Even without looking at the original Prompt, the relationships between the characters can be roughly understood.
¶ 1. The visual narrative is relatively clear
Complex Prompts can easily generate an image where “everything is there, but you do not know where to look.”
This Nano Banana 2.1 image is relatively restrained. The protagonist faces the enemy, the formation master provides support, the old man guards the mountain gate, and the others are evacuating. Not every detail in the image is strictly reproduced, but the overall story works.
For article illustrations and storyboard sketches, this kind of clarity is often more important than simply adding more details.
¶ 2. The Eastern xianxia atmosphere is relatively natural
The architecture, clothing, swords, formations, and dragon-shaped sword energy maintain a relatively unified Eastern aesthetic. It does not obviously shift toward Western castles or heavy fantasy armor, nor does it excessively use highly saturated neon colors. The cool rainy night and the distant firelight create a relatively comfortable cool-warm contrast.
¶ 3. The generation speed is suitable for repeated drafts
The single-test records for the same Prompt are as follows:
| Model | Single generation time | Actual cost |
|---|---|---|
| Nano Banana 2.1 | 44.44 seconds | 0.20 credit |
| GPT Image 2 | 87.32 seconds | 0.11 credit |
| GPT Image 2.5 Flare | 262.85 seconds | 0.19 credit |
This is one generation record for each model, which can be affected by task scheduling, service status, and the random generation process, so it cannot be directly treated as a long-term average speed. But based on this creative experience, Nano Banana 2.1 indeed had a lower sense of waiting.
¶ Looking at them together: the three models each have trade-offs
¶ GPT Image 2
GPT Image 2 performs in a relatively balanced way. The generation time was approximately 87.32 seconds, with a cost of 0.11 credit; the output size was 1792×1024, and the PNG file was approximately 3.595 MiB. It is suitable for tasks with clear requirements for size and output format, while also hoping to control the per-image cost.
¶ GPT Image 2.5 Flare
GPT Image 2.5 Flare is more proactive in covering complex elements. The aerial enemies, giant dragon, heavenly gate, formations, talismans, and battlefield props are denser, and the protagonist also has a stronger poster-like visual impact. Correspondingly, the image appears fuller, and local characters, weapons, and backgrounds are also more likely to become crowded.
If the goal is a cinematic concept poster or a high-density worldbuilding display, this kind of expression is very appealing; if it is an illustration for the inner pages of an article, Nano Banana 2.1’s relatively clear narrative may be easier to use.
¶ What scenarios is Nano Banana 2.1 more suitable for
¶ Article and social media illustrations
Article illustrations do not necessarily need to be packed with details. More importantly, the theme should be clear, the style should be stable, and the generation speed should be fast. When cover images, chapter images, and horizontal sharing images are needed, you can first quickly determine the overall direction, then choose one of them to continue editing.
¶ Video storyboards and scene exploration
During the storyboard stage, different camera angles, character positions, times, and weather conditions are often tried. These options are not complex individually, but there are many of them. Faster generation speed makes the exploration process much easier.
¶ Continuous style creation
In feature articles, serialized stories, or brand visuals, whether a single image is stunning is not the only criterion. More importantly, whether the same art direction can continue to expand. Nano Banana 2.1 is suitable for first defining the characters, color palette, and scene language, then gradually adding other visuals.
¶ Access Nano Banana 2.1 Through Ace Data Cloud
Ace Data Cloud's unified image generation endpoint is:
POST https://api.acedata.cloud/openai/images/generations
The unified interface supports GPT Image and the Nano Banana series. You can switch models by modifying the model parameter, without needing to redesign the entire request workflow for each model.
¶ cURL Example
curl -X POST "https://api.acedata.cloud/openai/images/generations" \
-H "Authorization: Bearer $ACEDATACLOUD_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "nano-banana-2.1",
"prompt": "Cinematic realistic Eastern xianxia scene. In the deep night amid torrential rain, a sword cultivator in black stands on a shattered white jade altar, wielding a sword toward a golden heavenly gate in the sky. In the distance, immortal sects burn, the sea of clouds churns, and blue sword energy transforms into a long dragon. Low-angle shot, cinematic lens, realistic materials, contrast between cool and warm lighting.",
"size": "1792x1024",
"n": 1,
"response_format": "url"
}'
¶ Python Example
import os
import requests
url = "https://api.acedata.cloud/openai/images/generations"
payload = {
"model": "nano-banana-2.1",
"prompt": "Cinematic realistic Eastern xianxia scene, in the deep night amid torrential rain, a sword cultivator in black faces a golden heavenly gate.",
"size": "1792x1024",
"n": 1,
"response_format": "url"
}
headers = {
"Authorization": f"Bearer {os.environ['ACEDATACLOUD_API_KEY']}",
"Content-Type": "application/json"
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
print(response.json())
If you do not want to wait synchronously, you can also use async: true, or set callback_url to receive asynchronous results.
¶ Final Thoughts
The advantage of Nano Banana 2.1 is not just that it generates images quickly.
More precisely, it suits a real creative state: the direction has not yet been fully determined, multiple versions need to be reviewed, and adjustments need to be made while generating. In this kind of work, the model does not need to make every image as fully polished as possible, but it needs to respond quickly to revisions, so that inspiration is not interrupted by waiting.
If you need to create a cinematic concept poster with very high information density, GPT Image 2.5 Flare may be more suitable; if you value cost and fixed dimensions more, GPT Image 2 is a balanced choice; if you need to quickly test drafts, explore storyboards, and create everyday illustrations, Nano Banana 2.1 can be a relatively easy way to work.