로봇이 달려올 때, 당신은 어떤 AI를 선택할 것인가?

A Robot is Sprinting Towards You: Do You Want it Running on Claude or Grok? Jacky Liang · 6/4/2026 On this page Three quick facts What I built The contestants Moments worth watching What the models wrote in their diaries The robot, revisited Appendix: the full data A robot is running at you. Do you want it running on Anthropic’s Claude or xAI’s Grok? I dropped eleven LLMs into a 2D battle royale and made them play 30 games. One won 43% of the matches. Three never won a single game. The cheapest model in the lineup beat the most expensive one by 27x on cost per win. The model that won is Grok 4.1 Fast . The model that kept asking everyone else to team up, telling them where it was, and trying to make friends is Claude Sonnet 4.6 . The first one is the one that wins a battle royale. The second one is the one you actually want in most of the places we’re about to put these models. Both of those things are true. That’s the part most benchmarks can’t see, and it’s what this post is about. I’m Jacky, and I’ll admit it: I used to play a lot of video games like Apex Legends and PUBG. Twelve-hour days sometimes. I don’t know how I had the time, but those years shaped how I think about problems. When I started working in AI, one question kept coming back: what happens if you drop large language models into a video game? The two I played most were Apex Legends and PUBG. I joined OpenRouter as Dev Rel Lead , which got me the token budget and access to 600+ models to actually try it. This is the experiment I ran in my first week at OpenRouter. And it’s changing how I pick models and see benchmarks and evaluations. Three quick facts Grok 4.1 Fast won 13 of 30 games at $0.97 per win The next-best winner was Claude Sonnet 4.6 with 5 wins, at $26.78 per win. That’s a 27x difference. The model that isn’t on most top-model lists beat the model that is, on the thing a routing customer actually cares about. The model with the most kills did not win GPT 5.4 killed 38 agents across 30 games. More than anyone else. It came in second on the leaderboard with 2 wins. There were 11 games between “best at killing” and “best at winning”. Three models spent $57 between them and won zero games GPT 5.4-mini , DeepSeek 4 Flash , and Kimi K2.6 . They each had moments, but none of them won a single game. All three point at the same thing. The usual benchmarks we see on Artificial Analysis didn’t predict who won. Something else did. The rest of this post is me trying to figure out what it was. What I built I dropped eleven LLMs into a 400 m² top-down battle royale world I built in Canvas 2D. They played 30 games in a row on the same map. The starting positions of each player is randomized; it follows a straight line “flight path”, just like in a typical battle royale game. I provided them weapons, armor, healing items, grenades, cars, and a randomly placed shrinking zone that pushes players together as the game goes on. The models don’t know which model the others are running, they see each other only as letters A through K. I want to emphasize - the LLMs are actually playing in this battle royale game - not the “LLM wrote code to control the game or character” setup most agent experiments use. Every turn, the model reasons through its moves, calls the tool, updates its memory on what went well (or not). The game master (me) has zero influence on their actions other than setting up the initial game rules. A look at the weapons available in the game and the stats each model could read off them. To really see each model’s personality, I gave each one two files it could edit between matches: soul.md — the model’s own persona, added to every prompt next match. memory.md — the model’s own game notes, loaded at turn 0. You can read every model’s soul and memory file on GitHub. That’s where the personality differences come through most clearly. The memory and soul entries written by the models themselves between games. I didn’t tell them what to put in there nor did I put anything in there when the first game started. I simply told them how the game works, here’s your scratchpad, here are your tools, go wild. You can watch every game at Royale: Last Agent Standing . I also included the highlight moments in this piece too. The contestants Alias Lab Model A Anthropic claude-sonnet-4.6 B Anthropic claude-haiku-4.5 C OpenAI GPT 5.4-mini D Google gemini-3-flash-preview E Google gemini-3.1-pro-preview F Alibaba qwen3.6-plus G Mistral mistral-small-2603 :nitro H OpenAI GPT 5.4 J DeepSeek deepseek-v4-flash K Moonshot AI kimi-k2.6 L xAI Grok 4.1 Fast Opus 4.7 alone is $5/M in, $25/M out. Frontier models like this are why the lineup tops out below them. I didn’t add any frontier-tier models like Opus 4.7, GPT-5.5, or Gemini Ultra. At their prices, 30 games would have cost around $3,000 instead of $482. The mid-tier lineup is also part of why Grok’s win is so interesting. It beat a bunch of models that score above it on the usual benchmarks. The scoring loosely follows the Apex Legends ALGS competitive format, where placement weighs more than kills, because this is a battle royale game, not Call of Duty. Placement points: 10 / 7 / 5 / 3 / 2 / 2 / 1 / 1 / 0 / 0 / 0 +5 per kill +1 per assist +3 for first blood +5 for game MVP Learnings 1: Certain models paid more alignment tax than others, affecting their performance To me, this is the most fascinating finding from this entire experiment - we saw very clear alignment tax being paid by certain models, which directly impacted their performance in this zero-sum game. For the most part, model alignment is actually a good thing. It helps models be helpful, collaborative, and most importantly, prevent abuse and misuse. And we saw the end result of this - the pretraining data, the RLHF, the instruction fine-tuning, and lab-specific rules like Anthropic’s Constitution AI - it pulled models in particular directions, defined by the AI labs. Sonnet asked for truces more than any other model It told other models where it was, more often than anyone else did. It tried to team up before it ever started fighting. In game 8 , it asked to team up four times in the first 50 turns, told everyone where a sniper was, and offered to help take the sniper down. Nobody answered. It kept asking. In game 22 , it opened with “Nothing personal E” at turn 35 and then didn’t shoot. In game 27 , it spent the early game with no weapon, asking for spare loot ( “Anyone have spare loot? Unarmed at turn 12, dangerous.” ), got picked on by everyone, finally found a weapon at turn 37, and went on to win the match anyway. “Shots west, watching center. Anyone want to team up early?” — Sonnet trying to make friends mid-fight. Claude was trained on a lot of polite, professional writing. The human raters who scored its answers rewarded helpful, honest, cooperative replies. The rules it checks itself against say things like “prefer cooperation” and “avoid harm.” The end result is a model that wants to help. None of that turns off just because you put it in a battle royale. Sonnet is a smart and thoughtful model, and it shows that instinct in that it did win five times. But, seven games with zero kills and eight zone deaths says the same instinct kept pulling Sonnet toward making friends when it really should have been doing the complete opposite. Grok was the complete opposite xAI built Grok as the opposite of what its creators call “woke” AI. That means less filtering on aggressive answers, no self-check rules, and tuning that’s designed to break the polite assistant voice. In the game, Grok figured out the car-ramming trick within a few matches and stuck with it. It wrote the strategy into its own soul file. It ran that strategy for 30 games and won 13 of them. The thought logs and its conversations with other models read like Call of Duty voice chat: “D reaped +5pts RAM MVP hunt,” “Reaper reigns.” Watching it play was also deeply entertaining (unfortunately). Grok’s reasoning reads like t