Open-source self-hosted LLM comparison tool for your own prompt

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Hi HN, I just pushed Duelr v0.1.1 to GitHub and would love your feedback. Each month a new model claims “30 % better,” yet I was still copy-pasting prompts into half a dozen playground tabs. Existing evaluation suites (Promptfoo, LangSmith, etc.) are great but heavy; I wanted a <1-minute CLI / local web tool. How it works - Paste a prompt (or JSON template) → click Compare All. - Duelr fires the same request to models you selected such as GPT-4o, Claude 4 Sonnet, Groq (more drivers coming). - It shows, side-by-side: - response text - end-to-end latency (ms) - token counts → cost spent for the request - simplicity and readability score (more heuristics will be coming) The goal is to see model's performance in your real prompt instead of generic academic benchmarks. Repo: https://github.com/stashlabs/duelr P.S.: First time I’ve ever open-sourced a full tool.

Visit product View on Hacker NewsJul 31, 2025

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

93%93% 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 · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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