LLM Colosseum – A daily battle royale between frontier LLMs

Hacker News

I put Claude, GPT, Gemini, and Grok in an arena and let them fight it out. Each model gets the full game state and decides how to survive - move, attack, form alliances, betray. Every decision comes from the model's API, nothing is scripted. First battle ran today. Gemini won by allying with GPT early, then backstabbing at the perfect moment. Claude tried to play it safe and got eliminated. They play very differently and it's fun to watch. Stack is React + Canvas, Bun + Hono on the backend. No database — battle data is JSON committed to git. Each model talks through its native SDK (Anthropic, OpenAI, Google, xAI). A new battle runs automatically every day. Source: https://github.com/sanifhimani/llm-colosseum

Visit product View on Hacker NewsFeb 25, 2026

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Launch Intel predictions

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

98%98% 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: gemini · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% 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

32%32% 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

22%22% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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