
FLUX.1-Kontext-dev API, Deployment, Pricing
black-forest-labs/FLUX.1-Kontext-dev
FLUX.1 Kontext [dev] is a 12 billion parameter image editing model developed by Black Forest Labs. Based on advanced Flow Matching technology, it functions as a diffusion transformer capable of precise image editing based on text instructions. The model's core feature is its powerful contextual understanding, allowing it to process both text and image inputs simultaneously and maintain a high degree of consistency for characters, styles, and objects over multiple successive edits with minimal visual drift. As an open-weight model, FLUX.1 Kontext [dev] aims to drive new scientific research and empower developers and artists with innovative workflows. Users can leverage it for various tasks, including style transfer, object modification, background swapping, and even text editing
Details
Model Provider
Black Forest Labs
Type
image
Sub Type
image-to-image
Publish Time
Jun 27, 2025
Price
$
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/ Image
Tags
MoE,235B,128K
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FLUX.1-Kontext-dev
FLUX.1-Kontext-dev
FLUX.1 Kontext [dev] is a 12 billion parameter image editing model developed by Black Forest Labs. Based on advanced Flow Matching technology, it functions as a diffusion transformer capable of precise image editing based on text instructions. The model's core feature is its powerful contextual understanding, allowing it to process both text and image inputs simultaneously and maintain a high degree of consistency for characters, styles, and objects over multiple successive edits with minimal visual drift. As an open-weight model, FLUX.1 Kontext [dev] aims to drive new scientific research and empower developers and artists with innovative workflows. Users can leverage it for various tasks, including style transfer, object modification, background swapping, and even text editing
FLUX.1-Kontext-dev

Qwen-Image-Edit
Qwen-Image-Edit
Qwen-Image-Edit is the image editing version of Qwen-Image, released by Alibaba's Qwen team. Built upon the 20B Qwen-Image model, it has been further trained to extend its unique text rendering capabilities to image editing tasks, enabling precise text editing within images. Furthermore, Qwen-Image-Edit utilizes an innovative architecture that feeds the input image into both Qwen2.5-VL (for visual semantic control) and a VAE Encoder (for visual appearance control), achieving capabilities in both semantic and appearance editing. This allows it to support not only low-level visual appearance edits like adding, removing, or modifying elements, but also high-level visual semantic editing such as IP creation and style transfer, which require maintaining semantic consistency. The model has achieved state-of-the-art (SOTA) performance on multiple public benchmarks, establishing it as a powerful foundation model for image editing
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