I query multiple LLMs in parallel because I don't trust any single one

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I have a mass of AI subscriptions. ChatGPT, Claude, Perplexity, Gemini. My workflow became: ask Claude, then paste the same question into ChatGPT to sanity-check, then maybe ask Perplexity if I need sources. Five tabs, constant copy-pasting. Council just runs your prompt against multiple models at once and shows responses side-by-side. That's it. A few things I noticed while building this: 1. Models disagree with each other way more than I expected. Ask anything slightly subjective or recent, and you'll get meaningfully different answers. It's made me much more skeptical of treating any single response as "the answer." 2. Different models have different failure modes. Claude tends to be cautious and hedge. GPT is confident even when wrong. Perplexity gives sources but sometimes misreads them. Seeing them together makes these patterns obvious. 3. For code, I actually like getting 2-3 different approaches. Even if one is clearly better, seeing alternatives helps me understand the tradeoffs. Tech: Next.js, OpenRouter for model access, streaming responses in parallel. The annoying part was handling the UI when models respond at different speeds – you don't want the layout jumping around. No login required to try it. Feedback welcome, especially on what's broken or annoying. https://usecouncil.app/

Visit product View on Hacker NewsJan 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: answers, way, para · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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