Models
Alibaba
Qwen Image Edit 2511

Alibaba

llms.txt

Qwen Image Edit 2511

Name: Qwen Image Edit 2511
Brand: Alibaba
Price: 0.03 USD
Availability: InStock

AI Image Editing Model

Image

Alibaba's Qwen image editing model for instruction-based image modifications and transformations

Example outputs coming soon

Try Model Benchmark examples

Supported Modes

Image Edit

Active

Details

Model ID

qwen-image-edit-2511

Also known as: qwen-image-edit-plus-2511

Creator

Alibaba

Family

qwen

Released

December 2025

Ready to integrate?

Access qwen-image-edit-2511 via our unified API.

Create Account

Available at 2 providers

Starting from

$0.030 /image via fal.ai, Replicate

Prices shown are in USD · Some prices estimated from per-megapixel or per-token pricing

See all providers

Provider Performance

Fastest generation through fal at 4,728ms median latency with 100.0% success rate.

Aggregated from real API requests over the last 30 days.

Generation Time

fal

p95: 10,291ms

replicate

p95: 11,808ms

Success Rate

fal

100.0%

18 / 18 requests

replicate

85.2%

23 / 27 requests

Time to First Byte

fal

3,405ms

p95: 8,258ms

replicate

4,946ms

p95: 11,984ms

Provider Rankings

#	Provider	p50 Gen Time	p95 Gen Time	Success Rate	TTFB (p50)
1	fal	4,728ms	10,291ms	100.0%	3,405ms
2	replicate	6,127ms	11,808ms	85.2%	4,946ms

Data updated every 15 minutes. Based on all API requests through Lumenfall over the last 30 days.

Providers & Pricing (2)

Qwen Image Edit 2511 is available from 2 providers, with per-image pricing starting at $0.03 through fal.ai.

fal.ai

fal/qwen-image-edit-2511

Provider Model ID: fal-ai/qwen-image-edit-2511

$0.030 /megapixel

View pricing details

Replicate

replicate/qwen-image-edit-2511

Provider Model ID: qwen/qwen-image-edit-2511

$0.030 /image

View pricing details

qwen-image-edit-plus-2511 API OpenAI-compatible

Integrate Qwen Image Edit 2511 through the Lumenfall API to perform instruction-based image editing and text-to-image generation using a single OpenAI-compatible endpoint. This interface allows developers to programmatically modify existing visual assets or create new high-fidelity images via standard HTTPS requests.

Base URL

https://api.lumenfall.ai/openai/v1

Model

qwen-image-edit-2511

Code Examples

Image Edit

/v1/images/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=qwen-image-edit-2511" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'qwen-image-edit-2511',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.edit(

    model="qwen-image-edit-2511",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Edit instruction for the image	Edit
`negative_prompt`	string	Negative prompt to guide generation away from undesired content	Edit
`seed`	integer	Random seed for reproducibility	Edit

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9") `auto` `1365x768` `768x1365` `887x1182` `1024x1024` `1183x887` WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.	Edit
`aspect_ratio`	string	Aspect ratio of the output image (e.g. "16:9", "1:1") `auto` `9:16` `3:4` `1:1` `4:3` `16:9` Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.	Edit
`resolution`	string	Output resolution tier (e.g. "1K", "4K") `auto` `1K` Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.	Edit

Output	`size`		`aspect_ratio` + `resolution`
Flexible
Auto	`"auto"`	—	Model chooses optimal dimensions
Custom 1–14142px per side	`"WxH"`	—	Any pixel dimensions within model constraints

1K 5 sizes

Output	`size`		`aspect_ratio` + `resolution`
1183 × 887	`"1183x887"`	or	`"4:3"` + `"1K"`
1024 × 1024	`"1024x1024"`	or	`"1:1"` + `"1K"`
887 × 1182	`"887x1182"`	or	`"3:4"` + `"1K"`
768 × 1365	`"768x1365"`	or	`"9:16"` + `"1K"`
1365 × 768	`"1365x768"`	or	`"16:9"` + `"1K"`

How these parameters work

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

Media Inputs

Parameter	Type	Description	Modes
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP.	Edit

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	Edit
`output_format`	string	Output image format `png` `jpeg` `gif` `webp` `avif` Gateway converts to requested format if provider doesn't support it natively.	Edit
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	Edit
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	Edit

Additional Parameters

Parameter	Type	Description	Modes
`cfg_scale`	number	Classifier-free guidance scale — higher values stick more closely to the prompt	Edit
`acceleration` fal	string	The acceleration level to use. `high` `none` `regular`	Edit
`disable_safety_checker` replicate	boolean	Disable safety checker for generated images.	Edit
`enable_safety_checker` fal	boolean	If set to true, the safety checker will be enabled.	Edit
`go_fast` replicate	boolean	Run faster predictions with additional optimizations.	Edit
`num_inference_steps` fal	integer	The number of inference steps to perform.	Edit
`output_quality` replicate	integer	Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs.	Edit
`sync_mode` fal	boolean	If `True`, the media will be returned as a data URI.	Edit

Parameter Normalization

How we handle parameters across different providers

Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:

Behavior	What happens	Example
`passthrough`	Sent as-is to the provider	style, quality
`renamed`	Same value, mapped to the field name the provider expects	prompt
`converted`	Transformed to the provider's native format	size
`emulated`	Works even if the provider has no concept of it	n, response_format

Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.

Full API Reference Authentication, endpoints, and more

Qwen Image Edit 2511 Benchmarks

Qwen Image Edit 2511 currently holds the #2 rank in the Image Editing arena with an Elo rating of 1230. This position makes it one of the top-performing models globally for instruction-based image modifications and transformations.

Lumenfall Arena

#13

Image Editing

1195 Elo

Image Editing Landscape

Elo vs Cost

Elo vs Speed

1 model waiting for enough speed data

Competition Results

Image Editing

Photorealism

View leaderboard

Man and Car in California

13 models

Image Editing

Source

Edit instruction

“Make a photo of the man driving the car down the California coastline”

Qwen Image Edit 2511 edited result for Bald man challenge

Original image before Qwen Image Edit 2511 editing

Before After

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Qwen Image Edit 2511 edited result for Neutral Expression to Genuine Smile

Before After

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

Qwen Image Edit 2511 edited result for Night Sky Transformation

Before After

#11

Night Sky Transformation

16 models

Image Editing

Edit instruction

“Change the scene to night: a deep, dark sky with subtle, glistening stars visible behind the mountain.”

Golden Hour Stroll

13 models

Image Editing

Source

Edit instruction

“Add dynamic motion to this photo: make hair blow in the wind, add leaves flying, energetic and lively feel.”

Image Editing

Anime

View leaderboard

Studio Ghibli Anime Style

14 models

Image Editing

Source

Edit instruction

“Transform this photo into a Studio Ghibli–inspired illustration. Use soft pastel colors, hand-painted textures, gentle lighting, dreamy backgrounds, and a warm, nostalgic mood”

Image Editing

Portrait

View leaderboard

Before After

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Before After

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

Top Matchups

See how Qwen Image Edit 2511 performs head-to-head against other AI models, ranked by community votes in blind comparisons.

vs GPT Image 1.5

Challenge: Man and Car in California

32% W · 66% L · 2% T

vs Nano Banana

Challenge: Neutral Expression to Genuine Smile

22% W · 67% L · 11% T

vs Reve Image 1.0

Challenge: Night Sky Transformation

17% W · 83% L

vs Wan 2.6

Challenge: Golden Hour Stroll

20% W · 80% L

vs Nano Banana

Challenge: Bald man challenge

0% W · 100% L

Use Cases

See all Use Cases

The model demonstrates significant strength in Photorealism, ranking #5 out of 16 models with a 56.4% win rate when generating lifelike visual assets. It excels at complex image editing tasks where precise adherence to natural language instructions is required.

#10 of 15

Portrait

Edit · 40.4% win rate

#10 of 14

Anime

Edit · 35.4% win rate

#12 of 16

Photorealism

Edit · 50.8% win rate

Gallery

View all 7 images

Qwen Image Edit 2511 FAQ

How much does Qwen Image Edit 2511 cost?

Qwen Image Edit 2511 starts at $0.03 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use Qwen Image Edit 2511 via API?

You can use Qwen Image Edit 2511 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "qwen-image-edit-2511". Code examples are available in Python, JavaScript, and cURL.

Which providers offer Qwen Image Edit 2511?

Qwen Image Edit 2511 is available through fal.ai and Replicate on Lumenfall. Lumenfall automatically routes requests to the best available provider.

Overview

Qwen Image Edit 2511 is a specialized vision-language model developed by Alibaba designed for instruction-based image modification. Unlike standard text-to-image models that generate images from scratch, this model takes an existing image and a natural language prompt as input to perform precise transformations. It is distinctive for its ability to follow complex editing instructions while maintaining the spatial consistency and identity of the original subject.

Strengths

Instruction Following: Translates nuanced natural language commands into specific visual changes, such as “make the sky a sunset” or “replace the coffee cup with a glass of orange juice.”
Subject Preservation: Maintains the high-level features and structural integrity of the base image, ensuring that modified elements blend realistically with the unchanged surroundings.
Style and Texture Transfer: Excels at altering the artistic style or material properties of an image while keeping the underlying geometry intact.
Localized Editing: Demonstrates the ability to target specific regions for modification without requiring the user to provide manual masks or pixel-perfect coordinates.

Limitations

Heavy Morphological Changes: While effective at replacement and style shifts, it may struggle with extreme structural changes that fundamentally alter the perspective or anatomy of the primary subject.
Text Rendering: Like many diffusion-based architectures, it may produce illegible or inconsistent text when asked to add specific typography to an image.
Prompt Sensitivity: Drastic changes in the prompt can occasionally lead to unintended global shifts in color or lighting that stray from the original image’s mood.

Technical Background

Qwen Image Edit 2511 belongs to the broader Qwen family of models, leveraging a multi-modal architecture that bridges visual encoders with a generative backbone. It is trained on large-scale datasets of paired images (before and after) and their corresponding textual descriptions to learn the relationship between linguistic instructions and visual deltas. This approach allows the model to treat image editing as a conditional generation task, focusing on the residuals between the source and target states.

Best For

This model is ideal for creative asset iteration, rapid prototyping of social media content, and product visualization where specific attributes must be toggled (e.g., changing background environments or colors). It is also well-suited for developers building photo editing tools that require a natural language interface.

Qwen Image Edit 2511 is available for integration and testing through Lumenfall’s unified API and playground, allowing you to benchmark its editing precision against other generative vision models in your workflow.

Try Qwen Image Edit 2511 in Playground

Generate images with custom prompts — no API key needed.

Open Playground