Black Forest Labs

FLUX 1 Kontext Pro

Name: FLUX 1 Kontext Pro
Author: Black Forest Labs

FLUX.1 Kontext Pro is an image generation and editing model from Black Forest Labs, part of the FLUX.1 Kontext line that accepts both text and image inputs for in-context edits.

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Prices updated daily. Last check: Nov 9, 2026

Model Details

General

Creator: Black Forest Labs
Modalities: Text

Capabilities

Open Source: No
Aliases: FLUX.1 Kontext Pro

Strengths & Limitations

Strengths

Accepts an input image alongside a text instruction, enabling edits without separate masking or inpainting pipelines
Handles both text-to-image generation and image editing through a single model interface
Designed for iterative, multi-turn editing where a result is fed back in for further changes
Aims to preserve subject and scene consistency across successive edit passes
Mid-tier Pro positioning within the Kontext line offers a middle point between lighter and heavier variants
Available through Black Forest Labs' API and multiple third-party inference providers, so pricing and regions can be compared

Limitations

Image generation is billed per image or per generation, which makes cost forecasting different from token-based models
We do not track a published benchmark suite for this model, so quality comparisons rely on hands-on testing
Maximum output resolution and aspect ratio support vary by hosting provider and are not tracked here
Instruction-based editing can still drift on fine details such as small text or faces across many sequential edits
As a Pro-tier variant, it is positioned below the larger option in the same line for the most demanding fidelity work

Key Features

•Text-to-image generation

•Image editing from an input image plus a text instruction

•In-context prompting that combines reference imagery with written directions

•Iterative multi-pass editing workflow support

•Character and scene consistency handling across edits

•Local edits (object, background, or style changes) without an explicit mask

•Available via Black Forest Labs API and third-party inference hosts

•Part of the FLUX.1 Kontext family at the Pro tier

About FLUX 1 Kontext Pro

FLUX 1 Kontext Pro is an image model from Black Forest Labs, the developers of the FLUX.1 family. It sits in the Kontext branch of that family, which is oriented around "in-context" work: rather than only turning a text prompt into a new picture, the model also takes an existing image as part of the prompt and applies instructions to it. The Pro designation marks the mid-tier option in the Kontext lineup, positioned between the lighter and heavier variants Black Forest Labs offers. Because it handles both text-to-image and image-plus-text-to-image requests through the same interface, FLUX 1 Kontext Pro is commonly used for iterative editing workflows — changing an object, swapping a background, altering style, or adjusting text within a scene while trying to keep the rest of the frame and the subject's identity consistent across passes. It is served through Black Forest Labs' own API as well as a number of third-party inference providers, several of which appear in the pricing table on this page. Compared with pure text-to-image systems, the distinguishing behavior here is instruction-driven editing on a supplied reference image, which removes the need for separate inpainting masks or control models for many common edits. Buyers evaluating it typically weigh output fidelity and prompt adherence against per-image cost and generation latency, since image models are billed per image or per generation rather than per token. We do not track detailed benchmark scores, maximum output resolution, or licensing terms for this model, so verify those details with the provider you intend to use.

Common Use Cases

FLUX 1 Kontext Pro suits production image workflows where an existing asset needs to be modified rather than regenerated from scratch: e-commerce product shots that need background swaps or seasonal restyling, marketing creative where one hero image must be adapted into many variants, concept art iteration where an artist refines a composition step by step, and social or ad creative pipelines that generate large batches with a consistent character or brand look. The image-plus-instruction input also makes it a fit for automated pipelines that take user-uploaded photos and apply a defined transformation. For work that only needs novel images from a text prompt and no editing, a plain text-to-image model may be sufficient; for the highest-fidelity output in this line, teams often test the larger Kontext variant alongside Pro before committing.

Frequently Asked Questions

What is FLUX 1 Kontext Pro best used for?

It is best used for instruction-driven image editing — taking an existing image and applying changes such as object replacement, background swaps, or style shifts — as well as standard text-to-image generation. Its iterative editing behavior makes it a fit for variant generation and step-by-step creative refinement.

How much does FLUX 1 Kontext Pro cost?

Pricing varies by provider and by pricing type, and image models are typically billed per generated image rather than per token. Check the pricing table on this page for current rates across the providers we track.

How is Kontext different from other FLUX.1 models?

The Kontext branch is built around in-context prompting: it accepts an input image together with a text instruction, so it can edit as well as generate. Other FLUX.1 variants are oriented primarily toward text-to-image generation.

Can it edit an image I supply, or only create new ones?

Both. You can pass a reference image with a text instruction to have the model apply the requested change, or send a text prompt alone to generate a new image.

What resolution can it output?

Maximum resolution and supported aspect ratios depend on the hosting provider and its API settings, and we do not track those figures. Confirm the limits with the specific provider you plan to use.

Is FLUX 1 Kontext Pro the right tier for my project?

Pro is the mid-tier option in the Kontext line. If your work needs the highest output fidelity, compare it against the larger variant; if throughput and per-image cost matter more than maximum detail, test a lighter variant first. Running the same prompts through each tier is the most reliable way to decide.