AI app for it and development · no coding needed
Model comparison and selection workspace
Reduce selection time while producing a documented, reviewable comparison.
Made for: Product and engineering teams choosing between AI models or options for a defined task
What it does for you
The problem
Model choices are made from scattered benchmarks, vendor claims and ad-hoc trials that are hard to reproduce or defend.
What it gives you
Reviewed selection report linked to raw evidence
What you give it
Task casesevaluation criteriamodel accesscost constraintspermitted source files
Build your own version of thisorthis.ai, MIOSN and more
One app with what these 6 AI tools do, yours to keep and change: thisorthis.ai, MIOSN, Langtail 1.0, Aymo AI, Contentable.ai, LLM Stats.
Everything these tools do, in one app
- Side-by-side model comparison Allows users to compare outputs or performance of multiple AI models simultaneously.Found in MIOSN, Langtail 1.0, Aymo AI and 1 more
- Community voting and feedback Enables gathering opinions from a community to aid decision-making.Found in thisorthis.ai
- Customizable evaluation criteria Lets users define their own metrics or priorities for assessment.Found in MIOSN
- Spreadsheet-like testing interface Provides a grid format to create, run, and score test cases easily.Found in Langtail 1.0
- Multi-model access Offers access to a wide range of AI models in one place.Found in Aymo AI, LLM Stats
- AI content generation Generates written content such as articles and social media posts.Found in Contentable.ai
- Real-time results display Shows results immediately as they are updated.Found in thisorthis.ai
- Shareable polls and apps Allows creating and sharing polls or AI apps via links or social media.Found in thisorthis.ai, Langtail 1.0
- Interactive playground Provides an environment to test prompts against models.Found in LLM Stats
- API access Enables programmatic access to models for automation and integration.Found in LLM Stats
- Team collaboration Facilitates sharing and collaboration among team members.Found in Aymo AI
- File analysis Accepts various file types for context-aware answers.Found in Aymo AI
- Chrome extension Integrates AI capabilities into any webpage via a browser extension.Found in Aymo AI
- Customizable tone and style Adjusts output to match specific brand voices or writing styles.Found in Contentable.ai
- Grammar and readability checks Enhances text quality by checking grammar and readability.Found in Contentable.ai
- Content planning and suggestions Assists with brainstorming and planning content ideas.Found in Contentable.ai
- Leaderboards and benchmarks Provides community-driven rankings and performance benchmarks.Found in LLM Stats
- Cost metrics Shows cost information such as cost per 1k tokens for budgeting.Found in LLM Stats
How it works, step by step
- Compare outputs from multiple AI models side by side
- Collect community votes and structured feedback on candidate outputs
- Define custom evaluation criteria and weights
- Run test cases in a spreadsheet-like grid and score them
- Access a range of AI models from one workspace
- Generate written content for test cases and drafts
- Display results in real time as they update
- Share polls, comparisons and apps by link
- Test prompts against models in an interactive playground
- Expose an API for programmatic runs and integration
- Support team collaboration and shared review
- Analyze uploaded files for context-aware answers
- Capture AI assistance from a browser extension on any webpage
- Adjust output tone and style to a defined voice
- Check grammar and readability of generated text
- Suggest content plans and ideas for test cases
- Show leaderboards and benchmarks for candidate models
- Report cost metrics such as cost per 1k tokens
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed selection report linked to raw evidence with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Model comparison and selection workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Model comparison and selection workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data194 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce selection time while producing a documented, reviewable comparison. For product and engineering teams choosing between AI models or options for a defined task, convert task cases, evaluation criteria, model outputs and community feedback into a reviewed selection report linked to raw evidence. The benefit is a testable hypothesis, measured through accepted selection decisions per evaluation hour and rework after model choice; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect task cases, evaluation criteria, model access and cost constraints, then follow this sequence: 1. Compare outputs from multiple AI models side by side. 2. Collect community votes and structured feedback on candidate outputs. 3. Define custom evaluation criteria and weights. 4. Run test cases in a spreadsheet-like grid and score them. 5. Access a range of AI models from one workspace. 6. Generate written content for test cases and drafts. 7. Display results in real time as they update. 8. Share polls, comparisons and apps by link. 9. Test prompts against models in an interactive playground. 10. Expose an API for programmatic runs and integration. 11. Support team collaboration and shared review. 12. Analyze uploaded files for context-aware answers. 13. Capture AI assistance from a browser extension on any webpage. 14. Adjust output tone and style to a defined voice. 15. Check grammar and readability of generated text. 16. Suggest content plans and ideas for test cases. 17. Show leaderboards and benchmarks for candidate models. 18. Report cost metrics such as cost per 1k tokens. Resolve uncertain cases with qualified reviewers, approve reviewed selection report linked to raw evidence, and measure accepted selection decisions per evaluation hour and rework after model choice against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One defined task family and approved model list; final selection and production decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and publication scope. One defined task family and approved model list; final selection and production decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One defined task family and approved model list; final selection and production decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: compare outputs from multiple AI models side by side; collect community votes and structured feedback on candidate outputs. Support the remaining modules with operator review: define custom evaluation criteria and weights; run test cases in a spreadsheet-like grid and score them. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned task cases, authorized model endpoints and permitted research sources. Cloud storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Task and criteria setup, Side-by-side comparison grid, Selection report and delivery. Use a thumbnail gallery for evaluation projects, a large central comparison grid, and a right-hand panel for criteria, cost metrics and comments. Let users compare model outputs side by side. Display draft, changes requested and approved states. Provide a shareable comparison link with comments anchored to the relevant test case. Make the task-specific outcome reviewed selection report linked to raw evidence visible beside its evidence, review state and value baseline.
