Better Than 4 But Not 5 – An LLM model ordering challenge

Hacker News

This simple game challenges you drag-and-drop a set of LLM models into the correct order (release date, input cost at launch, LM Arena score) using just their name. It's no secret that LLM model names are a bit of a mess, but when OpenAI decided to backtrack from 4.5 to 4.1, well, I just couldn't let it go. This was my first attempt at vibe coding something with VSCode Copilot Agent (w/ Claude 3.7 Sonnet). The agent got me like 95% functionality, but the moment I needed to crack open the code myself I couldn't handle the mess and had to clean it up. I am bad at letting go control enough for vibe coding. If anyone else has had that problem and gotten over it, I would love some helpful tips. I collected the data primarily by digging through Simon Willison's blog archives and snapshotting LMArena's leaderboard. If you spot any inaccuracies in the model data, I'd appreciate corrections with sources! Nothing fancy in the tech here, just a vanilla HTML/CSS/JS site hosted on GitHub Pages. GitHub link (MIT License): https://github.com/wspittman/BetterThan4ButNot5

Visit product View on Hacker NewsApr 18, 2025

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Did not reach leaderboard

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

95%95% 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

67%67% predicted probability of success on Indie Hackers, 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

35%35% 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

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

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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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