Image Editing
Source
Edit instruction
“Make a photo of the man driving the car down the California coastline”
Alibaba's Qwen image editing model for instruction-based image modifications and transformations
Example outputs coming soon
Details
Model ID
qwen-image-edit-2511
Also known as:
qwen-image-edit-plus-2511
Creator
Alibaba
Family
qwen
Released
December 2025
Tags
image-generation
image-editing
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 providersProvider Performance
Fastest generation through fal at 4,368ms median latency with 100.0% success rate.
Aggregated from real API requests over the last 30 days.
Generation Time
fal
4,368ms
p95: 14,720ms
replicate
5,610ms
p95: 10,403ms
Success Rate
fal
100.0%
35 / 35 requests
replicate
88.6%
39 / 44 requests
Time to First Byte
fal
3,405ms
p95: 12,821ms
replicate
4,823ms
p95: 8,803ms
Provider Rankings
| # | Provider | p50 Gen Time | p95 Gen Time | Success Rate | TTFB (p50) |
|---|---|---|---|---|---|
| 1 | fal | 4,368ms | 14,720ms | 100.0% | 3,405ms |
| 2 | replicate | 5,610ms | 10,403ms | 88.6% | 4,823ms |
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
Replicate
replicate/qwen-image-edit-2511
Provider Model ID:
qwen/qwen-image-edit-2511
$0.030
/image
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/editscurl -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 OpenAIclient = 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
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9")
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")
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")
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
|
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:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
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.
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
#12
Image Editing
1200
Elo
Image Editing Landscape
Elo vs Cost
Elo vs Speed
1 without speed data omitted.
Competition Results
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"
}
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.”
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
Edit instruction
“Change the scene to night: a deep, dark sky with subtle, glistening stars visible behind the mountain.”
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
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"
}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.”
Top Matchups
See how Qwen Image Edit 2511 performs head-to-head against other AI models, ranked by community votes in blind comparisons.
Use Cases
See all Use CasesThe 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.
Gallery
View all 7 imagesQwen 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.