GPT Image 2.5 vs
Qwen Image 2.1

Choose by how you need to work: GPT Image 2.5 offers hosted generation and editing; Qwen Image 2.1 offers downloadable weights, multi-image editing and native RGBA workflows.

Capability comparison

Hosted generation and editing

GPT Image 2.5

Start in the browser without setting up a GPU.

OpenAI example: a retrofuturist space habitat

Flare for everyday generation
Sunburst for precise editing
Both variants support transparent output in the API

Use GPT Image 2.5

Local deployment and editing workflows

Qwen Image 2.1

Run the weights yourself and connect image-processing steps.

Qwen example: an outfit assembled from five references

Up to 10 reference images
Native RGBA generation and editing
ComfyUI, Diffusers and LoRA workflows

Explore Qwen Image 2.1

These are separate official examples with different inputs. They illustrate use cases and do not establish a quality ranking.

What do you need to do?

Select a task to see a practical starting point.

Start with GPT Image 2.5

Use the hosted workspace for posters, illustrations or a single-image edit. Start with Flare; try Sunburst when editing precision matters. You need an account and credits on Image Toolkit.

Open the generator

Try Qwen’s multi-image workflow

Combine a person, clothing, products and a scene from up to 10 references. GPT’s Image API also accepts multiple images, but the GPT workspace on this site currently accepts one reference per request.

Open the embedded Qwen demo

Both support transparency; choose your workflow

Qwen outputs native RGBA for local asset pipelines. GPT Image 2.5 supports transparent PNG or WebP through its API. This site’s GPT workspace does not currently expose a transparent-background control.

See Qwen’s transparent examples

Evaluate Qwen Image 2.1

Download the weights for local inference and use DiffSynth for LoRA training. Budget for the full pipeline, including Qwen3-VL and the VAE. Qwen’s research license requires separate permission for commercial use.

Read the local setup guide

Compare the capabilities that affect your workflow

Model and API capabilities are shown separately from the controls currently available on Image Toolkit.

Compare the capabilities that affect your workflow
Dimension	GPT Image 2.5	Qwen Image 2.1
Deployment	Hosted service through an API. No local weights are offered in the model documentation. OpenAI API details	Downloadable weights for local or self-hosted inference; official hosted demo also available. Qwen model details
Reference images	OpenAI’s Image edit API accepts up to 16 images. This site’s workspace accepts one. OpenAI API details	Up to 10 images, with instructions referring to each image by order. Qwen model details
Transparent output	Both Flare and Sunburst support transparent PNG / WebP via the API. OpenAI API details	Native RGBA VAE; generate transparent images or extract a subject into a transparent layer. Qwen model details
Local edits	Image API supports a mask. Sunburst is positioned for precise editing. No mask editor in this site’s GPT workspace. OpenAI API details	Select an area with circles, painted annotations or a separate mask, then describe the edit. Qwen model details
Resolution	API supports custom sizes; above 2560 × 1440 is experimental, up to 3840 × 2160 within pixel and edge limits. OpenAI API details	Native 2K in the official release. Diffusers defaults to a 1024 target side; dimensions can be set explicitly. Qwen model details
Customization	The official model pages do not support fine-tuning. OpenAI API details	DiffSynth supports LoRA and full training, including RGBA images. Qwen model details
Hardware	Generation runs in the cloud; no local GPU required.	7B refers to the visual generator, not the whole pipeline. Memory also depends on the encoder, resolution, references and offloading. Qwen model details
Commercial use	Subject to the terms of the service used; this site provides access through WaveSpeed.	Qwen Research License Agreement: non-commercial research and evaluation; a separate license is required for commercial use. Qwen license

What does it cost to use?

Hosted credits and local GPU costs are different billing models. Compare the total cost of the workflow you will actually run.

GPT Image 2.5 on Image Toolkit

credits / image

GPT Image 2.5 on Image Toolkit — credits / image
Output preset	Generate	Edit
1K	12	15
2K	45	50
4K	80	85

One PNG per task. Flare and Sunburst use the same site prices. These are Image Toolkit credits, not OpenAI API rates. The workspace shows the charge before generation.

View credit packs and plans

Qwen Image 2.1 locally

Costs depend on your GPU or cloud instance, resolution, number of references, offloading and throughput. Downloadable weights do not imply free compute or commercial permission.

The embedded demo runs on Qwen’s Hugging Face Space. Availability, queues and any usage limits are managed by the Space.

Try the embedded demo

Before you choose

Which produces better-looking images?

This page compares documented capabilities and the site’s available controls. We have not run a controlled head-to-head benchmark, so it does not claim an overall quality winner. Test your own prompts and judge text accuracy, subject details and editing consistency.

Does Qwen’s 10-reference support mean GPT only accepts one?

No. One reference is the limit of Image Toolkit’s current GPT workspace. OpenAI’s Image edit API supports up to 16 input images; Qwen documents up to 10. Input count alone does not measure reference fidelity.

How should I compare them on my own task?

Use the same prompt, reference files, aspect ratio and output size wherever supported. Save several results and record model variant, settings, elapsed time and cost. Check logos, faces, text spelling, unchanged regions and alpha edges against the originals.