ABrush vs Numerous.ai: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ABrush and Numerous.ai — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

ABrush

Freemium

AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.

Photoshop-native panel: Generation, editing and upscaling happen on the open document and land on real layers, with no export-import round trip
23+ models in one panel: Switch between Stable Diffusion, Flux, Qwen Image and others per stage of a piece rather than committing to one provider
Targeted editing: Inpaint or regenerate only the region that needs changing, keeping the rest of the composition untouched
Pro conditioning controls: ControlNet support plus IP-Adapter and reference images for pose, composition and style control
Custom LoRA styles: Load your own LoRA or style models to keep generations consistent with an established look
Generation history: Every generation is saved and recoverable, so artists can return to an earlier variation without regenerating
Shareable presets: Save prompts and settings as presets and share them across a team to reproduce a house style
Commercial-safe data policy: Generated images belong to the user and customer images are not used for model training

A concept artist generating multiple variations of a character directly in the working file and painting over the strongest one
A retoucher fixing a single element of a composite with inpainting rather than regenerating the whole image
A studio distributing a shared preset pack so several artists produce work in a consistent house style
A freelance illustrator using a custom LoRA to keep generated assets on-style with a client's brand
A designer upscaling and cleaning up a low-resolution asset without leaving Photoshop
An agency handling commercial client work that needs assurance the images aren't used for model training

Freemium

A spreadsheet plugin that embeds ChatGPT into Google Sheets and Excel to extract text, categorize data, and generate formulas.

Chat Integration: Embed ChatGPT-style conversational prompts directly in Google Sheets and Excel cells to query, transform, and generate content from spreadsheet data.
Formula Generation: Automatically produce complex spreadsheet formulas from natural-language descriptions, reducing time spent crafting and debugging formulas manually.
Text Extraction and Categorization: Extract structured text from messy cells, categorize free-form responses (e.g., survey replies), and populate consistent labels across rows.
Batch Processing: Apply transformations, prompts, or categorization rules across entire ranges or columns to automate repetitive data-cleaning and classification tasks.
Summaries and Natural-Language Reports: Generate row-, column-, or sheet-level summaries and human-readable explanations of datasets for faster insights and reporting.
Cross-platform Support: Works inside both Google Sheets and Microsoft Excel, enabling teams to use the plugin across common spreadsheet environments.
Bring ChatGPT functionality directly into Google Sheets and Excel
Extract text from spreadsheet cells using natural-language prompts
Categorize and label rows or cells based on content
Generate spreadsheet formulas and functions via natural language
Enable in-sheet AI-assisted data-cleaning and analysis

Survey Analysis: Automatically categorize open-ended survey responses into topics or sentiment labels and create summary counts within the spreadsheet.
Formula Assistance: Convert a plain-language calculation request into a working spreadsheet formula (e.g., VLOOKUP, INDEX/MATCH, nested IFs) to accelerate spreadsheet building.
Data Cleaning: Extract and standardize text from inconsistent cells (addresses, product descriptions, notes) across large ranges using natural-language prompts.
Report Generation: Produce executive-ready summaries and written explanations of sales, financial, or operational data directly from sheet data.
Bulk Labeling: Apply consistent tags or classifications to thousands of rows (e.g., product categories, customer types) with a single prompt and range selection.
Ad-hoc Analysis: Ask natural-language questions about a dataset (e.g., 'Which regions underperformed last quarter?') and receive interpreted results or formulas inserted into the sheet.
Automatically extract structured information from messy text in cells
Categorize product descriptions, support tickets, or survey responses
Generate or suggest Excel/Sheets formulas based on plain-language requests
Accelerate spreadsheet data-cleaning and transformation tasks
Embed conversational or generative assistance into spreadsheet workflows