Nano Banana vs FLUX.1 Kontext vs Qwen Image Edit

Short answer: pick Nano Banana when you need to blend many reference images into one picture or generate images in volume; pick FLUX.1 Kontext when you need precise local edits over several rounds while the rest of the image stays unchanged; pick Qwen Image 2.0, which carries Qwen Image Edit's editing capability, when you need 2K output, Chinese-language work or several candidates per request. All three are available on APIMart under one account, so you can test them side by side with the same assets.

All three are leading examples of "edit an image with one sentence." Nano Banana is Google's Gemini 2.5 Flash Image, released in August 2025. FLUX.1 Kontext is the in-context image editing model Black Forest Labs launched in May 2025. Qwen Image Edit is the editing model Alibaba's Qwen team open-sourced in August 2025; with Qwen-Image-2.0 in February 2026, generation and editing were merged into a single model. Each makes a different trade-off: Nano Banana leans on multi-image fusion and world knowledge, Kontext on consistency in local edits, and Qwen on resolution and its open-source ecosystem.

Key Takeaways

• Reference image limits differ the most: Nano Banana accepts up to 14 reference images, FLUX.1 Kontext up to 4, and Qwen Image 2.0 up to 3 on APIMart.
• Output size: Nano Banana is fixed at 1K with 10 aspect ratios; Kontext outputs about 1 megapixel with 9 aspect ratios; Qwen Image 2.0 supports 1K and 2K with 7 aspect ratios.
• Billing: Nano Banana’s official route bills actual input and output tokens; Ext bills per successfully generated image. Kontext (Pro / Max) and Qwen Image 2.0 (Standard / Pro) bill per image. Check the model pages for current rates.
• Local edits: Kontext [pro] is officially positioned for local and multi-turn editing, and APIMart measured about 6 seconds per image.
• Multiple images per call: only Qwen Image 2.0 returns 1–6 images per request; Nano Banana and Kontext return 1.
• Open source: the Qwen-Image-Edit series is Apache 2.0; FLUX.1 Kontext [dev] has open weights under a non-commercial license; Nano Banana is closed.

The Three Models at a Glance

How Are Nano Banana, FLUX.1 Kontext and Qwen Image Edit Different?

Nano Banana: Multi-Image Fusion and World Knowledge

Nano Banana is Google's name for Gemini 2.5 Flash Image. Its defining feature is that one request can take up to 14 reference images, blending people, products and scenes into a single frame while keeping a character consistent across generations. Because it is built on Gemini, it can use world knowledge to follow instructions such as "turn this into an 80s magazine cover" without spelling out every detail.

The trade-off is a single 1K output size, one image per call and a 1,000-character prompt limit. Google adds an invisible SynthID watermark to every image it generates or edits.

FLUX.1 Kontext: Local Edits and Multi-Turn Consistency

FLUX.1 Kontext is Black Forest Labs' "in-context" image model: the same model handles text-to-image and instruction-based edits to an existing picture. The official lineup positions [pro] as the main model for local and multi-turn editing, such as changing the color of a jacket or replacing a sign in the background while the rest of the frame stays largely untouched; [max] is the experimental tier with stronger prompt adherence and typography.

It accepts up to 4 reference images, with multi-image reference marked as experimental on the APIMart model page, and outputs about 1 megapixel. Speed is one of its strengths: APIMart measured about 6 seconds for [pro] and about 9 seconds for [max]. The open-weight [dev] version has 12 billion parameters and uses a non-commercial license, though the model card states that the generated images themselves can be used commercially.

Qwen Image Edit: Open-Source Editing and Qwen Image 2.0

Qwen-Image-Edit is an open-source editing model built on the 20-billion-parameter Qwen-Image, released on August 18, 2025 and updated monthly after that: version 2509 added multi-image editing, and version 2511 followed in December 2025. All of these versions are Apache 2.0 and can be self-hosted.

Qwen-Image-2.0, released in February 2026, merged generation and editing into one model, supports native 2K and understands instructions of up to about 1,000 tokens. APIMart does not offer a separate "Qwen Image Edit" model; editing is provided through Qwen Image 2.0, so you edit by passing in reference images. The Pro tier is stronger at text rendering and photorealistic detail.

Image Editing Capabilities Compared

Reference Images and Multi-Image Fusion

If your workflow is "one model + several products + one scene," only Nano Banana can take all of that material in a single request. If you are revising one image again and again, Kontext's limit of 4 is enough.

Output Size and Aspect Ratios

• Nano Banana: 1K, with 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 and auto.
• FLUX.1 Kontext: about 1MP, with 1:1, 4:3, 3:4, 16:9, 9:16, 3:2, 2:3, 21:9, 9:21 and auto; pixel sizes are mapped to the nearest ratio and cannot raise the output resolution.
• Qwen Image 2.0: 1K or 2K, with 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3.

If you need finished 2K images, only Qwen Image 2.0 outputs them directly; the other two need upscaling afterward. For 21:9 banners choose Nano Banana or Kontext, and for 9:21 tall images only Kontext supports the ratio.

Local Edits and Text

All three models support natural-language local edits such as "change the text on the mug to MORNING" or "remove the passer-by in the background." The difference is emphasis: Kontext treats "keep everything else unchanged" as its core selling point, which suits design drafts that go through several revisions; Nano Banana is better at understanding style-level and semantic instructions; Qwen Image 2.0 Pro is stronger at rendering text inside images and supports a negative prompt to exclude unwanted elements.

Billing Compared

For Nano Banana’s official route, a per-image estimate is not a fixed final price: billing depends on actual input and output token usage. Ext bills per successfully generated image. Qwen Image 2.0 charges the same price for 1K and 2K output. Check each model page for current rates and membership discounts; the final charge is determined when the task completes.

Testing Them Side by Side on APIMart

All three models share the same image generation endpoint and the same account on APIMart. Switching models means changing one model name, and reference images are passed the same way, so you can run the same assets through all three side by side. For request examples, parameter details and current prices, see the model pages linked above.

Which One Should You Use?

Choose Nano Banana When

• You need to combine more than 4 reference images into one frame.
• You need the same character or product to stay consistent across a set of images.
• You generate images in volume and 1K output is enough.

Choose FLUX.1 Kontext When

• You need several rounds of edits on the same image, changing one area at a time.
• You need tall formats such as 9:21.
• Speed matters, or you may later move to the self-hostable [dev] weights.

Choose Qwen Image 2.0 When

• You need 2K images straight from the model.
• You want several candidates from one request to pick from.
• You need Chinese text rendered inside images (the Qwen-Image team says its text rendering is especially strong for Chinese), or you value the open-source Qwen ecosystem.

Or Use Them Together

A common pattern is to do the multi-image composite with Nano Banana, hand the result to Kontext for local retouching, and redraw with Qwen Image 2.0 when you need a high-resolution final. All three share one account on APIMart, so combining them needs no extra integration.

FAQs

What is the main difference between Nano Banana, FLUX.1 Kontext and Qwen Image Edit?

Nano Banana supports up to 14 reference images and is best at multi-image fusion; FLUX.1 Kontext focuses on local edits and consistency across multiple rounds; Qwen Image Edit is an open-source editing model that APIMart serves through Qwen Image 2.0, with 2K output and several images per call.

Is Qwen Image Edit available on APIMart?

APIMart has no model named Qwen Image Edit. Qwen-Image-2.0 already merges generation and editing into one model, so passing reference images to Qwen Image 2.0 or Qwen Image 2.0 Pro on APIMart uses its editing capability.

How are the three models billed?

Nano Banana’s official route bills actual input and output tokens; Ext bills per successfully generated image. An official-route per-image estimate is not a fixed final price. FLUX.1 Kontext (Pro / Max) and Qwen Image 2.0 (Standard / Pro) bill per image, and Qwen Image 2.0 charges the same price for 1K and 2K. Check the model pages for current rates and membership discounts; the final charge is determined when the task completes.

Which model can output 2K images?

Of the three, only Qwen Image 2.0 supports 2K output, at the same price as 1K. Nano Banana is fixed at 1K, and FLUX.1 Kontext outputs about 1 megapixel.

How many reference images can I upload?

Nano Banana accepts up to 14, FLUX.1 Kontext up to 4, and Qwen Image 2.0 up to 3 on APIMart, which must be publicly accessible URLs.

Can I use these models commercially?

Images generated through APIMart can be used under each model provider's terms. If you self-host, the Qwen-Image-Edit series is Apache 2.0; the FLUX.1 Kontext [dev] weights are under a non-commercial license, but the model card states that the images it generates can be used commercially.

Do the output images carry a watermark?

Every image Nano Banana generates or edits carries Google's invisible SynthID watermark. For FLUX.1 Kontext and Qwen Image 2.0, refer to each provider's official documentation.

What to Watch Next

Google has already released Nano Banana Pro and Nano Banana 2, Qwen keeps iterating on the Qwen-Image series, and the next generation of FLUX is on the way. The gap between image editing models is shifting from "can it edit" to "how stable are the edits, how many references can it take and how large an image can it produce." Run your own real assets through all three models, then pick your main model based on results, visual consistency and the current prices on the model pages.