PQ Apps
PromptQuorum, wherever you work
One prompt, dispatched to 25+ AI models at once — with local LLM support and multi-model consensus analysis, all from your browser.
Try BetaHow It Works
A 4-stage workflow: write a structured prompt using one of 9 frameworks, optimize it with your own LLM, dispatch simultaneously to 25+ AI services, then analyze all responses using 13 consensus analysis types.
Structure Your Prompt
Prompts structured with frameworks produce higher quality outputs. PromptQuorum includes 9 built-in frameworks (Single Prompt Line, CRAFT, CO-STAR, RISEN, TRACE, APE, SPECS, Google Prompt, RTF) plus 2 fully custom framework slots.
- ✓Single Prompt Line — minimal structure for quick tasks
- ✓CRAFT — Context, Role, Action, Format, Target (creative writing)
- ✓CO-STAR — Context, Objective, Style, Tone, Audience, Response (marketing, business)
- ✓RISEN — Role, Instructions, Steps, End Goal, Narrowing (sequential enterprise tasks)
- ✓TRACE — Task, Request, Action, Context, Example (few-shot learning)
- ✓APE, SPECS, Google Prompt, RTF — optimized for specific task types
Refine with Your Own LLM
Prompt quality improves measurably with optimization — structured prompts score 25–45% higher in LLM evaluation. PromptQuorum applies 8 refinement types (Make Concise, Expand Detail, Break Into Steps, Increase Specificity, Simplify, Add Quality Controls, Multi-Expert Consultation, Compress to Essence) plus smart temperature detection.
- ✓Quality Assessment — 0-100% scoring on clarity, specificity, structure, and constraints
- ✓Smart Temperature — recommends optimal creativity level (0.0-1.0) based on task type
- ✓Version History — every refinement saved; branch and compare refinement paths
- ✓Teaching Mode — explains why each change improves quality and clarity
- ✓8 One-Click Refinements — apply structured transformations instantly
- ✓Custom Instruction — free-text refinement using your own LLM
Send to 25+ AI Services
Sending the same prompt to multiple AI models reveals which model performs best for your task. PromptQuorum opens parallel browser tabs to 25+ destinations with zero copy-pasting required.
- ✓Auto-dispatch (17 services): OpenAI ChatGPT, Google Gemini, Anthropic Claude, Perplexity, xAI Grok, DeepSeek, Mistral, Cohere, Azure, Together, Groq, and more
- ✓Copy-paste (8 services): Qwen, Meta AI, Poe, Kimi, LM Studio, Jan AI, GPT4All, and others
- ✓Perplexity auto-submits — prompt sent immediately on arrival
- ✓2 custom URL slots — configure any AI service not on the default list
- ✓Optional pre-dispatch refinement — final LLM enhancement before sending
- ✓Parallel execution — all tabs open simultaneously; collect responses in under 1 minute
Find Consensus Across All Models
When 5+ independent models agree on an answer, confidence is higher than with a single model. Paste all responses back into PromptQuorum and apply 13 consensus analysis types.
- ✓Consensus Summary — identifies shared themes and unanimous agreements
- ✓Contradiction Detection — flags where models diverge; identifies minority opinions
- ✓Hallucination Detection — identifies claims appearing in few models; potential false facts
- ✓Confidence Scoring — certainty level per model and per claim
- ✓Best Answer Selection — selects the highest-quality individual response
- ✓Weighted Merge — synthesizes a hybrid response using best elements from all models
9 Built-in Prompt Frameworks
Structured prompts using frameworks produce measurably better outputs than unstructured requests. Each framework organizes input differently for specific task types. A Framework Wizard recommends the best fit, or build 2 custom frameworks.
| Framework | Optimal For |
|---|---|
| Single Prompt Line | Quick, ad-hoc queries without structure |
| APE | 3-field minimal structure; simple tasks |
| CRAFT | Creative writing; general-purpose tasks |
| CO-STAR | Marketing copy; business communication |
| SPECS | Analysis; research; technical writing |
| RISEN | Multi-step enterprise workflows |
| TRACE | Few-shot learning; example-based tasks |
| Google Prompt | Professional tasks; role-based prompts |
| RTF | Minimal structure; 3 core fields only |
13 Quorum Analysis Types
Apply 2 or all 13 analyses to responses from multiple models. Each analysis is executed by your connected LLM, not PromptQuorum servers. Identify consensus, contradictions, hallucinations, and confidence levels across all model outputs.
- →Consensus Summary — shared themes across all models
- →Weighted Merge — hybrid answer combining best from each model
- →Atomic Facts Extraction — break all claims into discrete facts; count model agreement
- →Overlap Mapping — identify which models produced identical outputs
- →Contradiction Detection — flag claims where models diverge; identify disagreements
- →Confidence Scoring — measure certainty level per model and per claim
- →Completeness Check — verify all required information is present
- →Hallucination Detection — identify claims appearing in few models; potential false facts
- →Redundancy Elimination — remove duplicate or near-duplicate claims
- →Best Answer Selection — pick the single highest-quality response
- →Multi-Model Ensemble — combine outputs using model reliability weighting
- →Controversy Flag — highlight claims where model agreement is weak
- →Custom Analysis — user-defined analysis template
Multiple formats → downloaded as a .zip archive. File System Access API for folder selection (Chrome/Edge/Safari 16+).
Key Concepts
- Multi-Model Dispatch
- Sending one prompt simultaneously to 25+ AI models in a single click. PromptQuorum pre-loads your prompt into each destination via URL — no copy-pasting, all tabs open in parallel.
- Quorum Analysis
- Structured comparison of responses from multiple AI models to identify consensus, contradictions, and confidence levels. PromptQuorum offers 13 analysis types including Hallucination Detection and Best Answer Selection.
- Consensus Scoring
- A confidence rating derived from the degree of agreement across multiple model responses. Higher consensus = higher reliability. Lower consensus flags areas of uncertainty or potential hallucination.
- Hallucination Detection
- Identifying factual claims that appear in only one or a minority of model responses, indicating potential AI fabrication. Cross-referencing 5+ independent models dramatically reduces the rate of undetected hallucinations.
- BYOM — Bring Your Own Model
- Connecting your own API keys directly to AI providers. Keys are stored only in your browser's localStorage and connect directly to providers — no PromptQuorum server ever receives or transmits your credentials.
Bring Your Own Model (BYOM) — No PromptQuorum Infrastructure
PromptQuorum does not host or execute any LLM models. Every API call goes directly from your browser to your chosen provider. Your API keys stay in browser localStorage and are never transmitted to PromptQuorum servers.
- OpenAI (GPT-4, GPT-4o)
- Anthropic (Claude 3.5)
- Google Gemini 1.5
- Grok (xAI)
- DeepSeek
- Mistral
- Cohere
- Together AI
- Groq
- OpenRouter (free tier)
- Ollama (localhost:11434)
- LM Studio (localhost:1234)
- Jan AI (localhost:1337)
- GPT4All (localhost:4891)
- Open WebUI
- KoboldCpp
- vLLM
- oobabooga
- Any OpenAI-compatible endpoint
No telemetry
No analytics, tracking, logging, or data collection. Not even anonymous usage statistics or session timing.
No registration
Zero signup required. No email, no account, no login. Open the app; start immediately.
Offline-capable
Desktop app (Electron) and mobile app (Capacitor) support full offline operation with local models via Ollama, LM Studio, Jan AI, or compatible endpoints.
How We Test
Performance claims in PromptQuorum articles are based on controlled dispatching sessions using PromptQuorum. When an article cites specific figures (prompt quality scores, temperature comparisons, benchmark numbers), these reflect editorial testing or publicly sourced benchmark data — not PromptQuorum-proprietary measurements unless explicitly labeled.
- →Prompt dispatch: prompts are sent simultaneously to the stated models via PromptQuorum one-click dispatch
- →Sample size: editorial tests use a minimum of 30 prompts per condition unless the article states otherwise
- →Evaluation: responses are scored by at least 2 independent reviewers under blind conditions
- →Third-party benchmarks (HumanEval, SWE-bench, MBPP): sourced from official model papers or community leaderboards; evaluation date cited in each article
- →Local model tests: run on consumer hardware at the quantization level stated in the article
- →Disclosure: wherever PromptQuorum internal testing is cited, it is labeled "Tested in PromptQuorum" in the article body
Features
Key Features at a Glance
- ✓9 prompt engineering frameworks (CO-STAR, CRAFT, RISEN, TRACE, APE, SPECS, Google, RTF)
- ✓Dispatch to 25+ cloud models simultaneously (GPT-4o, Claude, Gemini, DeepSeek, and more)
- ✓13 Quorum consensus analysis types across 4 categories (synthesis, comparison, quality, selection)
- ✓Hallucination detection flags claims that appear in only one model or contradict consensus
- ✓Local LLM support: Ollama, LM Studio, Jan AI, GPT4All, Open WebUI, vLLM, and any OpenAI-compatible endpoint
- ✓Privacy-first: full offline execution, zero registration required, nothing leaves your device
- ✓Instant side-by-side response comparison across all dispatched models in real-time
- ✓Automatic prompt optimization with 8 refinement techniques for better AI output
Prompt Optimization
Automatically refine and optimize your prompts with 8 proven refinement techniques for better AI output.
Multi-Model Dispatch
Run prompts across ChatGPT, Claude, Gemini, and 25+ other AI models simultaneously in parallel.
Quorum Scoring
Find consensus answers across models with confidence scoring. Hallucination Detection flags claims that appear in only one model response.
Instant Comparison
Get parallel responses in one click — no manual copy-pasting between browser tabs.
Privacy-First
Local execution option. Zero registration required. Complete control over your prompts.
Prompt Optimizer
Choose a framework, optimize your prompt, and compare across AI models
Selected provider
OpenAI GPT-4
💡 Tip: Be specific about your requirements, context, and desired output format.
📚 Need help optimizing your prompt? View prompt engineering best practices
How Do You Review Optimization Results?
Review quality assessments, version history, and improvement suggestions for your optimized prompts.
Optimization Results
Review, refine, and optimize your prompt with AI assistance
Original Prompt
Optimized Prompt
Quality Assessment
- • Clear structure with numbered sections
- • Concrete examples provided for beginners
- • Actionable techniques listed
- • Good use of formatting (bullets, emphasis)
- • Could include more diverse examples
- • Interactive elements would enhance engagement
- • Transition between sections could be smoother
Version Control
Track all iterations of your prompt. Revert to previous versions anytime or branch off to explore different optimization paths.
Quality Insights
Understand exactly why your prompt was improved. Get detailed feedback on strengths and areas to refine.
Smart Refinements
Apply one-click refinements to make your prompt concise, clear, professional, or more detailed as needed.
What Is Quorum — Multi-Model Consensus?
Collect responses from 25+ AI models, analyze consensus patterns, and synthesize insights across different perspectives.
Quorum — Multi-Model Consensus
Collect responses from multiple LLMs, analyze patterns, and synthesize insights across models.
Step 3: Analysis Results
Collect Responses
Run your prompt across ChatGPT, Claude, Gemini, and 25+ other models. Get diverse perspectives and responses instantly.
Analyze Patterns
Identify what all models agree on (consensus), where they differ, and which responses are highest quality for your use case.
Synthesize Insights
Combine the strengths of multiple models into better answers. Export results in multiple formats for further use.
Compare Tools
What is a multi-LLM comparison tool?
A multi-LLM comparison tool sends the same prompt to multiple large language models simultaneously and displays the responses side by side, letting users evaluate differences in reasoning, accuracy, and style across AI systems — GPT-4o, Claude 4.6 Sonnet, Gemini 2.5 Pro, Mistral Large, and others — without switching tabs or repeating input.
No single AI model is authoritative for all tasks in 2026. GPT-4o, Claude 4.6 Sonnet, and Gemini 2.5 Pro each have different training data, architectural biases, and reasoning strengths. A response that looks correct from one model may be contradicted, qualified, or significantly expanded by another.
The five tools compared here represent the major approaches currently available: consumer platforms (Poe by Quora), community benchmarks (LM Arena), developer evaluation suites (OpenMark), unified multi-model workspaces (AiZolo), and consensus scoring platforms (PromptQuorum). Each serves a different workflow.
What are the key differences between 5 multi-LLM tools?
The table below compares all five tools across the features that matter most for professional multi-LLM workflows — simultaneous dispatch, consensus scoring, local LLM support, API key control, and pricing.
| Tool | Simultaneous dispatch | Consensus scoring | Local LLM | API key control | Pricing |
|---|---|---|---|---|---|
| PromptQuorum | ✓ Yes | ✓ Quorum Verdict | ✓ Ollama + LM Studio | ✓ Your keys | Free beta |
| Poe (Quora) | ~ Sequential / limited | ✗ No | ✗ Cloud only | ~ Limited | Free / $19.99/mo |
| LM Arena | ~ 2 models only | ~ Human voting only | ✗ Cloud only | ✗ No | Free |
| OpenMark | ✓ Parallel | ~ Deterministic scoring | ✗ Cloud only | ✓ Yes | Free tier / credits |
| AiZolo | ✓ Yes | ✗ No | ✗ Cloud only | ✓ Yes | From $9.90/mo |
✓ Yes · ~ Partial · ✗ No · Based on public documentation, March 2026. Pricing and features change — verify with each vendor. This comparison is produced by PromptQuorum.
PromptQuorum is the only tool among those reviewed that combines simultaneous prompt dispatch with automated consensus scoring. You write one prompt, select your models — GPT-4o, Claude 4.6 Sonnet, Gemini 2.5 Pro, Mistral Large, and locally-running models — and PromptQuorum dispatches to all of them in parallel. The Quorum Verdict then analyses where the models agree, where they diverge, and what those patterns mean for the reliability of the answer.
The defining feature is local LLM support. Via Ollama and LM Studio integration, PromptQuorum includes locally-running models — LLaMA 3.1 7B requires 8 GB RAM; 13B requires 16 GB — in the dispatch, so sensitive prompts never leave your machine. For legal professionals, healthcare workers, financial analysts, and developers working with proprietary code, this is not optional.
PromptQuorum requires users to bring their own API keys from OpenAI, Anthropic, Google, and Mistral. This keeps data under your control, costs transparent, and usage tied to your own commercial terms with each provider.
Who should use PromptQuorum?
PromptQuorum is designed for developers evaluating which model to integrate into a production pipeline, researchers who need cross-model validation of findings, and professionals whose work involves confidential information that cannot be sent to third-party cloud servers.
Poe, built by Quora, is the largest multi-model AI platform with access to GPT-4o, Claude 4.6 Sonnet, Gemini 2.5 Pro, Llama, Grok, and thousands of user-created bots from one interface. It is the best choice for users who want broad access to AI models without managing API keys.
Poe does not offer simultaneous dispatch — users switch between models or compare two at a time, rather than dispatching one prompt to all models in parallel. There is no consensus scoring or automated analysis of response agreement. All inference is cloud-based, making it unsuitable for privacy-sensitive work.
Poe vs PromptQuorum: key differences
Poe is better for casual exploration, bot discovery, and conversation without API key management. PromptQuorum is better for controlled prompt evaluation, consensus analysis, and local LLM workflows. They target fundamentally different use cases: Poe is a consumer platform; PromptQuorum is a professional evaluation tool.
LM Arena (formerly Chatbot Arena) is the most-cited AI model leaderboard, using Elo ratings derived from millions of human preference votes. Users submit prompts and vote on which of two anonymous models produced the better response.
LM Arena shows two models side by side and collects a human preference vote — it does not provide automated consensus analysis, does not support local LLMs, and does not allow selecting specific models in the primary comparison mode. It is a benchmarking platform, not a workflow tool.
LM Arena vs PromptQuorum: key differences
LM Arena is better for understanding aggregate human preference trends across the industry. PromptQuorum is better for evaluating your specific prompts across your chosen models with consistent, automated analysis. LM Arena tells you what the crowd prefers; PromptQuorum tells you what your prompt produces across every model you care about.
OpenMark is a developer-focused benchmarking tool that runs prompts against 100+ AI models simultaneously and scores results deterministically — the same prompt always produces the same ranked output. It shows exactly what each model costs per prompt alongside quality scores.
OpenMark is strong on breadth (100+ models) and cost transparency but does not produce a consensus verdict — it scores each model individually rather than analysing agreement patterns. It does not support local LLMs via Ollama or LM Studio.
OpenMark vs PromptQuorum: key differences
OpenMark answers "which single model performs best for this task and at what cost." PromptQuorum answers "how much do models agree on this prompt, and what does their disagreement mean?" Both require API keys; OpenMark supports 100+ models; PromptQuorum uniquely adds local LLM inference and consensus scoring.
AiZolo is a unified multi-model workspace designed for content creators and marketing teams, with simultaneous dispatch to GPT-4o, Claude 4.6 Sonnet, Gemini 2.5 Pro, and Grok side by side. As of March 2026, plans started from $9.90/month — verify current pricing at aizolo.com.
AiZolo does not offer consensus scoring — it displays responses side by side but leaves analysis to the user. It supports four cloud models only, with no local LLM option. It is a content production workflow tool, not a technical evaluation platform.
AiZolo vs PromptQuorum: key differences
AiZolo is better for content teams who need an affordable multi-model writing workspace for daily use. PromptQuorum is better for power users who need automated consensus analysis, local LLM privacy, and API-key-controlled access to a broader model set including open-weight systems.
Which multi-LLM tool should you use?
Frequently Asked Questions
Is PromptQuorum free?
Yes. PromptQuorum is free to use. You can bring your own API key, use a local LLM, or try our limited free backend service for prompt optimization on a test basis.
How does privacy work?
You decide where your data goes. Keep everything local with LM Studio or Ollama, or use your own API keys. PromptQuorum is as private as you set it up. Zero telemetry, zero tracking, no data collection — not even anonymous usage stats.
Which AI providers are supported?
Over 25 AI providers are included: OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google Gemini, Grok, DeepSeek, Mistral, Cohere, Together AI, Groq, OpenRouter, plus all local providers (Ollama, LM Studio, Jan AI, GPT4All, Open WebUI, KoboldCpp, vLLM, oobabooga, and any OpenAI-compatible endpoint).
What platforms does PromptQuorum run on?
PromptQuorum is available for macOS, Windows, and Linux (desktop via Electron). A web application is in development, followed by mobile (iOS and Android via Capacitor). It works fully offline with a local LLM.
What makes PromptQuorum different?
PromptQuorum covers the full prompt lifecycle in a single browser-based tool: structured writing with 9 frameworks, AI-powered iterative optimization with 8 refinement types, one-click dispatch to 25+ AI services, and 13 Quorum analysis types for consensus scoring — all without any data leaving your device.
Are there any limits?
No limits from PromptQuorum. Your usage is only limited by your API keys or local LLM resources.
What is prompt engineering and why does it matter?
Prompt engineering is the practice of designing inputs to AI models so they return more accurate, useful, and reliable outputs. In testing, structured prompts with framework fields produce 25–45% higher LLM evaluation scores compared to unstructured inputs. PromptQuorum automates this with 9 built-in frameworks — no expertise required.
How does PromptQuorum optimize my prompts?
Your connected LLM transforms raw framework fields into a precision prompt. You then refine iteratively with 8 one-click refinements: Make Concise, Expand Detail, Break Into Steps, Simplify, Increase Specificity, Multi-Expert Consultation, Add Quality Controls, and Custom Instruction. Every step is saved in version history so you can revert anytime.
What prompt frameworks are built into PromptQuorum?
PromptQuorum includes 9 frameworks: Single Prompt Line (quick), APE (3-field), CRAFT (creative writing), CO-STAR (won the Singapore GPT-4 competition), SPECS (analysis), RISEN (enterprise sequential tasks), TRACE (few-shot examples), Google Prompt (business tasks), and RTF (minimal 3-field). You can also build 2 fully custom frameworks.
What is the CO-STAR framework?
CO-STAR stands for Context, Objective, Style, Tone, Audience, and Response. It won the Singapore GPT-4 prompt engineering competition and is ideal for business communication, marketing copy, and content creation. PromptQuorum guides you through each field and assembles the final prompt automatically.
What is multi-model consensus and why is it valuable?
Multi-model consensus means sending the same prompt to multiple AI models and finding where they agree. When 5 independent models give the same answer, confidence is far higher than when 1 model answers alone. It also surfaces contradictions and potential hallucinations automatically.
How does PromptQuorum detect AI hallucinations?
After collecting responses from multiple models in the Quorum step, your LLM runs Hallucination Detection analysis — flagging claims that appear in only one model's response but not others, or that contradict factual consensus. You choose which analysis types to run and combine them freely.
Can I use PromptQuorum with local AI models like Ollama or LM Studio?
Yes. PromptQuorum natively connects to Ollama (localhost:11434), LM Studio (localhost:1234), Jan AI (localhost:1337), GPT4All (localhost:4891), Open WebUI, KoboldCpp, vLLM, oobabooga, and any OpenAI-compatible endpoint. No API key needed for local models — everything runs on your machine.
Can I use PromptQuorum completely offline?
Yes. If you run a local model like Ollama or LM Studio, PromptQuorum works fully offline. No internet connection is required. Your prompts, API keys, and results never leave your device.
What is BYOM (Bring Your Own Model)?
BYOM means PromptQuorum never calls any LLM using its own API keys. Every call goes directly from your browser to your chosen provider — cloud or local. Your API keys are stored only in your browser's localStorage and are never transmitted to any PromptQuorum server.
How does the Dispatch feature work?
Dispatch sends your optimized prompt to multiple AI services in one click. For auto-dispatch services (ChatGPT, Gemini, Perplexity, Claude, Copilot, DeepSeek, Mistral, and more), PromptQuorum pre-loads your prompt into the URL so it's ready instantly. Perplexity even auto-submits on arrival. All tabs open in parallel — collect all responses in under a minute.
What is the Quorum analysis and what types are available?
Quorum analysis processes all collected AI responses through your LLM. There are 13 analysis types across 4 categories: Synthesis (Consensus Summary, Weighted Merge, Atomic Facts Extraction), Comparison (Overlap Mapping, Contradiction Detection, Confidence Scoring), Quality (Completeness Check, Hallucination Detection, Redundancy Elimination), and Recommendations (Best Answer Selection, Multi-Model Ensemble, Controversy Flag).
Can I export my results?
Yes. Quorum results export in 6 formats: .txt, .md, .json, .csv, .html, and .pdf. Select multiple formats and they're bundled into a .zip archive. On Chrome, Edge, and Safari 16+, you can choose your save folder using the File System Access API.
How does the Framework Wizard work?
The Framework Wizard asks you a few questions about your task — what you're trying to achieve, the type of output you need, and your audience. Based on your answers it recommends the most suitable framework from the 9 built-in options, and shows a side-by-side comparison of what each would produce for your prompt.
What is Smart Temperature Adjustment?
Before each optimization, PromptQuorum analyzes your prompt text and suggests the ideal LLM temperature: ~0.2 for factual tasks, ~0.7 for balanced, ~0.85 for creative. It shows a confidence score and only prompts you when confidence is above 60%. After 3 consistent choices for the same intent type, it auto-applies your preference.
Does PromptQuorum work with ChatGPT, Claude, and Gemini?
Yes. You can use ChatGPT (GPT-4, GPT-4o), Anthropic Claude (3, 3.5), and Google Gemini (1.5 Pro, Flash) as your optimization LLM by adding your API key in Settings. You also dispatch prompts to all three simultaneously via the Dispatch page, without needing API keys for dispatch.
Is there a version history for my prompts?
Yes. Every optimization step and refinement is saved automatically in version history with a human-readable label (e.g. "v2 — Make More Concise 12:36"). You can select any version to restore it and branch new refinements from there. Nothing is lost.
What output formats and languages does PromptQuorum support?
The LLM output language is configurable per-session: English, German, French, Spanish, Italian, Portuguese, Chinese, and Japanese. Structure level options range from plain prose to strict tables-and-bullets. Response length is adjustable from 100 to 2000 words.
How does PromptQuorum handle my API keys securely?
API keys are stored only in your browser's localStorage — the same security boundary as your banking passwords stored in a password manager extension. They are never sent to any PromptQuorum server, never logged, and never included in telemetry (there is none). You can clear them anytime from Settings.
Is PromptQuorum suitable for enterprise or team use?
PromptQuorum is currently designed for individual power users — developers, researchers, content creators, and AI-heavy professionals. Each user runs their own instance with their own API keys. Enterprise features (shared workspaces, team history, role-based access) are on the roadmap.
What is Teaching Mode?
Teaching Mode adds an explanation box below every optimization result that explains exactly why each change was made — which prompt engineering principles were applied and what effect they have. It's designed for developers and researchers who want to learn prompt engineering while using the tool.
How do I get PromptQuorum and is there a cost?
PromptQuorum is in free public beta. Download the desktop app directly — no signup, no waitlist, no email required.
Who is Hans Kuepper, the founder of PromptQuorum?
Hans Kuepper is the founder and developer of PromptQuorum. He is based in Baden-Württemberg, Germany, near Heidelberg in the Kraichgau hill country. He speaks four languages — German, English, French, and Russian — and has lived and worked in over 20 countries.
Where is PromptQuorum developed?
PromptQuorum is built by Hans Kuepper, an independent developer in Baden-Württemberg, Germany. The project has no external investors and is developed as a privacy-first, user-owned AI tool.
What is the best tool to compare the same prompt across multiple LLMs simultaneously?
PromptQuorum is the only tool reviewed here that combines simultaneous dispatch with automated consensus scoring. Poe, AiZolo, and OpenMark offer parallel responses, but none produces a Quorum Verdict — an automated analysis of where GPT-4o, Claude 4.6 Sonnet, and other models agree or diverge. For users who need more than visual side-by-side comparison, PromptQuorum is the purpose-built option. Feature information verified March 2026.
What is the difference between PromptQuorum and Poe or LM Arena?
Poe (by Quora) is a consumer chat platform for switching between models one at a time. LM Arena uses crowdsourced voting to rank individual model performance. PromptQuorum is unique: it dispatches to all selected models simultaneously and automatically analyzes where they agree or diverge through consensus scoring. Poe is built for conversation; LM Arena for benchmarking; PromptQuorum for controlled evaluation and hallucination detection.