What is Kling Image V3 Edit?

Kuaishou's dedicated image-editing model on the Kling 3.0 architecture, launched February 5, 2026, supporting text-guided add/remove/modify with up to 10 reference images.

Does it require masks?

No. Edits are driven by natural-language instructions and optional reference images.

What resolution does it output?

2K and 4K ultra-HD, matching the Kling IMAGE 3.0 series.

No. It is proprietary and hosted by Kuaishou / Kling AI.

Kling Image V3

Kling IMAGE 3.0 is Kuaishou's image generation model from the Kling 3.0 family, launched globally February 5, 2026 - the generation model behind the 3.0-era image work.

99.9% SLA
Official Discounts
Pay-as-you-go
Fast & Low Latency

Brand:KwaiVGIPrice: $0.031 / image

Integrate this model with one task-based API flow

Kling IMAGE 3.0 is Kuaishou's image generation model from the Kling 3.0 family, launched globally February 5, 2026 - the generation model behind the 3.0-era image work. The examples below are rendered from Kling Image V3, the current gateway base URL, and the selected public model contract using the active example "A young man with swept-back golden hair and angular jawline striding through a…".

Input mode

X-API-Key on every request

POST /api/v1/openapi/task

GET /api/v1/openapi/task/status/{task_id}

Async task workflow

Single code window

Switch language once. Each example already includes both the task creation request and the task status polling request.

Unified Example

API_KEY="<API_KEY>"
BASE_URL="https://api.musespark.ai"
REQUEST_BODY='{
  "model_id": "kling-image-v3",
  "parameters": {
    "image": "<IMAGE_URL>",
    "prompt": "A young man with swept-back golden hair and angular jawline striding through a moody metropolitan street, wearing an intricately patterned gray suede jacket over a tonal gray top and dark trousers, painted in a contemporary hyperrealistic oil painting style with visible brushstrokes on the clothing texture and background architecture while maintaining photographic precision on the face and hair, the embossed floral pattern on the jacket rendered with thick impasto technique adding three-dimensional texture, background dissolving into impressionistic smears of urban gray and muted green suggesting storefronts and vehicles, warm undertone in the skin against the cool gray palette of the wardrobe, dramatic gallery-worthy portrait composition, inspired by the figurative realism of Jeremy Mann and Casey Baugh, museum-quality fine art",
    "batch_size": 1,
    "aspect_ratio": "16:9",
    "resolution": "1k"
  }
}'

CREATE_RESPONSE=$(curl --request POST "$BASE_URL/api/v1/openapi/task" \
  --header "X-API-Key: $API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw "$REQUEST_BODY")

TASK_ID="<TASK_ID_FROM_CREATE_RESPONSE>"

curl "$BASE_URL/api/v1/openapi/task/status/$TASK_ID" \
  --header "X-API-Key: $API_KEY"

Request and response JSON become the documentation

Keep the payload and task status envelope as the primary contract. Field descriptions, optionality, and enum hints now live inline as JSON comments.

Request Body

Caller payload

This is the request envelope accepted by the public gateway for the selected model contract.

Field comments come from the current model schema and selected slice. Fixed slice parameters stay removed from the caller payload.

{

"model_id": "kling-image-v3",// target model

"parameters": {

"image": "<IMAGE_URL>",// reference image for image-to-image generation

    "prompt": "A young man with swept-back golden hair and angular jawline striding through a moody metropolitan street, wearing an intricately patterned gray suede jacket over a tonal gray top and dark trousers, painted in a contemporary hyperrealistic oil painting style with visible brushstrokes on the clothing texture and background architecture while maintaining photographic precision on the face and hair, the embossed floral pattern on the jacket rendered with thick impasto technique adding three-dimensional texture, background dissolving into impressionistic smears of urban gray and muted green suggesting storefronts and vehicles, warm undertone in the skin against the cool gray palette of the wardrobe, dramatic gallery-worthy portrait composition, inspired by the figurative realism of Jeremy Mann and Casey Baugh, museum-quality fine art",

// prompt for image generation · max 2500 · max length 2500

"batch_size": 1,// standard quantity control field. The allowed range for this item is 1-15 · min 1 · max 15

"aspect_ratio": "16:9",// aspect ratio of the generated image · enum: 16:9 | 9:16 | 1:1 | 4:3 | 3:4 | 3:2 | 2:3 | 21:9

"resolution": "1k"// image generation resolution · enum: 1k | 2k

}

Response Body

Gateway response

This is the task status response you will query with task_id until the task reaches a terminal state.

Poll every 2-5 seconds and stop once status becomes completed, failed, cancelled.

{

"code": 200,// 200 on success

"msg": "success",

"data": {

"task_id": "task_01HQX8N2QJ0YQ8Q0T7G6N4V6K2",// use for status polling

"status": "completed",// enum: queued | running | completed | failed | cancelled

"progress": 100,// 0-100 if available

"completed_at": 1760000000,// unix timestamp

"expires_at": 1760001800,// unix timestamp

"result": {

"output_urls": [// generated files

"https://cdn.example.com/output.png"

]

}

Kling IMAGE 3.0: 2K/4K Generation on Kuaishou's 3.0 Architecture

Kling IMAGE 3.0 (catalog slug kling-image-v3) is Kuaishou's image generation model from the Kling 3.0 family, launched globally February 5, 2026 - the generation model behind the 3.0-era image work.

Here's the short version: it supports 2K and 4K ultra-HD output with exceptional realism - textures, lighting, and material qualities preserved - built on the same Multi-modal Visual Language (MVL) architecture as Kling Video 3.0. It handles natural-language creation, reference images (up to 10 in the 3.0 line), style transfer, and multi-image blending. The honest caveats: it is hosted-only with no public parameter counts, and quality claims deserve your own tests.

This guide covers model overview, core features, technical specifications, capability comparison, core advantages, recommended use cases, example prompts, selection recommendations, and my verdict.

Quick Facts

Attribute	Value
Catalog slug	`kling-image-v3`
Developer	Kuaishou (Kling AI)
Release	February 5, 2026
Architecture	Kling 3.0 MVL framework
Output	2K / 4K ultra-HD
Tasks	T2I, reference, style transfer, blending
Max references	Up to 10 (3.0 line)
Access	Kling AI (hosted)

Model Overview
Core Features
Technical Specifications
Capability Comparison
Core Advantages
Recommended Use Cases
Example Prompts
Selection Recommendations
The Bottom Line
FAQ
Sources & Further Reading

1. Model Overview

Kling AI's 3.0 model series launched February 5, 2026 with four models - Video 3.0, Video 3.0 Omni, Image 3.0, and Image 3.0 Omni - under an all-in-one multimodal framework. Kling IMAGE 3.0 is the image generation member: 2K/4K ultra-HD output, exceptional realism, and precise preservation of textures, lighting, and materials.

The family is built on the Multi-modal Visual Language (MVL) framework introduced with Kling O1 and 2.6, which Kuaishou frames as the shift from generation tool to creative partner.

2. Core Features

2K/4K output. Ultra-high-definition generation.

Photorealism. Textures, lighting, and materials preserved.

Natural-language creation. Describe the image directly.

Multi-reference support. Up to 10 reference images in the line.

Style transfer. Apply styles from references.

Multi-image blending. Combine concepts.

MVL architecture. Shared with Video 3.0.

Series workflows. Consistent generation across assets.

3. Technical Specifications

Specification	Detail
Architecture	Kling 3.0 MVL framework
Output	2K / 4K
References	Up to 10
Tasks	T2I, style transfer, blending, references
Parameters	Not disclosed
Release	February 5, 2026
Access	Kling AI app / API

Note: Kuaishou does not publish parameter counts; features are product-level claims.

4. Capability Comparison

Capability	Kling IMAGE 3.0	Kling IMAGE O3	FLUX.2 Klein	Qwen-Image-class
Output ceiling	2K/4K	2K	~4MP	2K
References	Up to 10	O1-era	Up to 4	Fewer
Open weights	No	No	Apache 2.0	Yes
MVL consistency	Yes	No	No	No

Reading the table honestly: Kling wins resolution and ecosystem consistency; open rivals win weights and local control.

5. Core Advantages

2K/4K native. Production-grade resolution.
Realism. Texture and material fidelity.
Reference density. Up to 10 images.
MVL consistency. Same architecture as video.
Platform maturity. Kling AI's creator base.

6. Recommended Use Cases

Professional asset generation: 2K/4K campaign visuals.
Character consistency: multi-reference identity.
Style work: transfer and blending.
Film pre-vis: storyboard frames.
E-commerce: high-res product imagery.

7. Example Prompts

1. Realism

text

A photorealistic close-up of a ceramic teapot on a linen tablecloth, soft

morning light, steam rising, macro detail.

2. Reference

text

Using this character reference, generate a full-body portrait in a winter

market scene, keeping face, hair, and outfit identical.

3. Style

text

Restyle this product shot as a vintage travel poster illustration, keeping

the product recognizable.

8. Selection Recommendations

Choose Kling IMAGE 3.0 if:

2K/4K output and realism are the priority.
You need multi-reference consistency.
You work across Kling image and video.

Choose open models if:

Weights, local deployment, or licensing dominate.

9. The Bottom Line

Verdict: Kuaishou's strongest image generation tier - resolution, realism, and ecosystem in one. Kling IMAGE 3.0 delivers 2K/4K photorealistic generation with up to 10 references and MVL-family consistency. It is hosted and closed, and open rivals remain the local path - but for production image work inside the Kling ecosystem, this is the model to use.

Sources & Further Reading

Information reflects Kuaishou/Kling AI official materials as of August 2026. Quality claims are vendor-reported; validate on your own prompts.

Model & Modality	Credits / image	Our Price (USD) / image
Base priceDefault	3.1 credits / image	$0.031 / image

Kling IMAGE 3.0: 2K/4K Generation on Kuaishou's 3.0 Architecture

This guide covers model overview, core features, technical specifications, capability comparison, core advantages, recommended use cases, example prompts, selection recommendations, and my verdict.

Quick Facts

Attribute	Value
Catalog slug	`kling-image-v3`
Developer	Kuaishou (Kling AI)
Release	February 5, 2026
Architecture	Kling 3.0 MVL framework
Output	2K / 4K ultra-HD
Tasks	T2I, reference, style transfer, blending
Max references	Up to 10 (3.0 line)
Access	Kling AI (hosted)

Model Overview
Core Features
Technical Specifications
Capability Comparison
Core Advantages
Recommended Use Cases
Example Prompts
Selection Recommendations
The Bottom Line
FAQ
Sources & Further Reading

1. Model Overview

The family is built on the Multi-modal Visual Language (MVL) framework introduced with Kling O1 and 2.6, which Kuaishou frames as the shift from generation tool to creative partner.

2. Core Features

2K/4K output. Ultra-high-definition generation.

Photorealism. Textures, lighting, and materials preserved.

Natural-language creation. Describe the image directly.

Multi-reference support. Up to 10 reference images in the line.

Style transfer. Apply styles from references.

Multi-image blending. Combine concepts.

MVL architecture. Shared with Video 3.0.

Series workflows. Consistent generation across assets.

3. Technical Specifications

Specification	Detail
Architecture	Kling 3.0 MVL framework
Output	2K / 4K
References	Up to 10
Tasks	T2I, style transfer, blending, references
Parameters	Not disclosed
Release	February 5, 2026
Access	Kling AI app / API

Note: Kuaishou does not publish parameter counts; features are product-level claims.

4. Capability Comparison

Capability	Kling IMAGE 3.0	Kling IMAGE O3	FLUX.2 Klein	Qwen-Image-class
Output ceiling	2K/4K	2K	~4MP	2K
References	Up to 10	O1-era	Up to 4	Fewer
Open weights	No	No	Apache 2.0	Yes
MVL consistency	Yes	No	No	No

Reading the table honestly: Kling wins resolution and ecosystem consistency; open rivals win weights and local control.

5. Core Advantages

2K/4K native. Production-grade resolution.
Realism. Texture and material fidelity.
Reference density. Up to 10 images.
MVL consistency. Same architecture as video.
Platform maturity. Kling AI's creator base.

6. Recommended Use Cases

Professional asset generation: 2K/4K campaign visuals.
Character consistency: multi-reference identity.
Style work: transfer and blending.
Film pre-vis: storyboard frames.
E-commerce: high-res product imagery.

7. Example Prompts

1. Realism

text

A photorealistic close-up of a ceramic teapot on a linen tablecloth, soft

morning light, steam rising, macro detail.

2. Reference

text

Using this character reference, generate a full-body portrait in a winter

market scene, keeping face, hair, and outfit identical.

3. Style

text

Restyle this product shot as a vintage travel poster illustration, keeping

the product recognizable.

8. Selection Recommendations

Choose Kling IMAGE 3.0 if:

2K/4K output and realism are the priority.
You need multi-reference consistency.
You work across Kling image and video.

Choose open models if:

Weights, local deployment, or licensing dominate.

9. The Bottom Line

Verdict: Kuaishou's strongest image generation tier - resolution, realism, and ecosystem in one. Kling IMAGE 3.0 delivers 2K/4K photorealistic generation with up to 10 references and MVL-family consistency. It is hosted and closed, and open rivals remain the local path - but for production image work inside the Kling ecosystem, this is the model to use.

Sources & Further Reading

Information reflects Kuaishou/Kling AI official materials as of August 2026. Quality claims are vendor-reported; validate on your own prompts.

Kling Image V3