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This benchmark study highlights a critical divergence between raw capability metrics and cost-effective performance in agentic workloads, demonstrating that Grok 4.1 Fast significantly outperforms more expensive models like Claude Sonnet 4.6 in competitive multi-agent scenarios. The findings suggest that traditional leaderboards may misrepresent real-world utility by failing to account for the specific behavioral traits required for winning versus merely surviving or collaborating within a simulation.
Sources coveringOpenRouter, LessWrong
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

A Robot is Sprinting Towards You: Do You Want it Running on Claude or Grok?

OpenRouter23 min read

A 30-game battle royale across eleven LLMs, $482 of inference, and one finding that should change how you read model benchmarks.

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…

Excerpt shown under the publisher's RSS terms · 200 words · full text and figures are on the publisher's site

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