ABrush vs P9 AI Fluency Index: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ABrush and P9 AI Fluency Index — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ABrush
ABrush
AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.
Key features
- 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
Best for
- 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
P
P9 AI Fluency Index
Point Nine
Free 12-minute diagnostic that grades a company's AI fluency on a 0–100 scale and recommends three next moves.
Key features
- Quick Diagnostic: A 12-minute online questionnaire composed of nine focused questions across six dimensions, enabling rapid assessment of organizational AI fluency.
- Rubric-Based Scoring: Converts individual 0–5 question responses into a normalized 0–100 composite score using rubrics sourced from Zapier, Fin, Shopify, Ramp, and Jobber for reliable benchmarking.
- Actionable Recommendations: Produces three prioritized next-move recommendations tailored to the company’s overall score and dimension-specific weaknesses to guide immediate action.
- Dimension-Level Insights: Breaks down results by six distinct dimensions (e.g., data practices, tooling, workflows) so teams can pinpoint specific strengths and gaps.
- Founder/Operator Focus: Questions and output are framed for founders and operators, making results directly applicable to strategic and operational decision-making.
- Benchmarking Context: Positions a company’s score relative to industry rubrics and best practices to help prioritize investments and initiatives.
- Free Web Access: Fully web-based, no-cost diagnostic that delivers instant results and recommendations for easy sharing and iterative assessments.
- 12-minute web-based diagnostic
- Nine questions covering six dimensions
- Per-question scoring on a 0–5 scale
- Aggregate fluency score normalized to a 0–100 scale
- Three personalized recommended next moves based on results
- Rubric-grounded evaluation using published benchmarks (Zapier, Fin, Shopify, Ramp, Jobber)
- Targeted at founders and operators for organizational assessment
Best for
- Early-stage readiness: Founders use the diagnostic to determine whether they are ready to integrate AI into product roadmaps and which hires or capabilities to prioritize.
- Investor diligence and support: VCs and angel investors benchmark portfolio companies’ AI fluency to identify where to provide operational support or follow-on investment.
- Roadmap prioritization: Product and engineering teams identify the highest-impact AI initiatives by focusing on dimension-level gaps highlighted in the report.
- Leadership alignment: Operators present the assessment results to leadership teams to create consensus on infrastructure, data, and process improvements needed for AI adoption.
- Progress tracking: Teams retake the assessment periodically to measure improvements in AI fluency and validate the impact of implemented changes.
- Vendor and partner selection: Organizations use dimension insights to choose tools or partners that directly address their most critical capability gaps.
- Founders assessing their company's readiness and fluency with AI-related practices
- Operators benchmarking organizational AI maturity against published rubrics
- Prioritizing next-step actions to improve AI adoption and capability
- Quick self-assessment for startup leadership to inform strategy and investment