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Black Forest Labs

FLUX.1 Kontext Max

flux-1-kontext-max

The flagship image editing version of the Kontext series, skilled at making local edits according to natural language instructions, with stronger prompt adherence and in-image text layout, suitable for brand visuals and batch storyboard production

flux-1-kontext-max

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Pricing

flux-1-kontext-max per image

Unit Price $0.0715

Positioning Overview

FLUX.1 Kontext Max is an image generation and editing model released by Black Forest Labs, accessible through the unified HiAPI.ai interface. It supports text-to-image generation and single-image editing: when an original image is provided, it modifies the specified content according to the prompt while preserving the rest; when the original image is omitted, it generates a new image from the prompt. Compared with lower-tier versions in the series, it is stronger at prompt adherence and in-image text layout, making it suitable for developers and content production teams that need precise control over the editing scope and visual consistency.

Key Highlights

  • Local editing that preserves unspecified regions — simply describe the colors, objects, background, or style to change in the prompt, and the rest of the image is kept as unchanged as possible.
  • Strongest prompt adherence and text layout in the series — as the top-tier version of the Kontext series, it executes complex editing intents and renders in-image text more accurately and cleanly.
  • One set of parameters for both text-to-image and image-to-image — within the same interface, toggling whether an original image is passed lets you choose between generating from scratch and editing based on an original image.
  • Adjustable aspect ratio and moderation strength — supports six output ratios, jpeg/png formats, and content moderation leniency levels from 0 to 2.

Capabilities

Natural-language local editing: pass a single original image and a prompt, and the model changes only the parts covered by the description while leaving unmentioned regions unchanged, ideal for rapid iteration on existing assets.

Text-to-image generation: when no original image is passed, it generates a brand-new image from the prompt, allowing the same interface to connect from-scratch generation and subsequent editing.

In-image text handling: the best text layout performance in the series, suitable for generating or modifying copy within images while keeping it readable and neat.

Ratio and format control: when editing, leaving the ratio empty retains the original image's ratio; specifying one crops or pads to the target ratio. Output can be jpeg or png for direct integration into downstream workflows.

Prompt rewriting toggle: enable prompt upsampling to add detail and creativity, or disable it to ensure strict generation from the original text.

Use Cases

  • Marketing teams modifying existing brand assets — input a finished poster or banner and specify the colors, objects, or background to replace, yielding variant images ready for publication.
  • Designers adjusting in-image copy — replace text and layout on existing visual drafts via prompts, producing finished images for ad placements or social media.
  • Storyboarding and multi-scene content production — edit a base image with the same style one by one to get a series of shots with consistent framing and style for storyboards.
  • Local replacement in product photos — upload a product photo and specify a new background or color scheme, producing finished images for e-commerce detail pages and display pages.

Limitations and Selection Advice

Only a single original image input is supported, so character consistency cannot be maintained through multi-image references; its positioning centers on image editing and modifying specified regions, and pure text-to-image is not its main strength. As a closed-source hosted model, it cannot be deployed locally and is not suitable for scenarios requiring self-controlled inference environments. Billing is per image, so cost must be factored in for high-volume, high-frequency editing; for everyday edits such as simple color or background adjustments, consider lower-tier versions in the same series to control expenses.

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