Magic has developed a pretraining recipe that is over 10x more compute-efficient than leading open-weight models, achieving DeepSeek V4 Pro Base performance with ~50x fewer FLOPs (approximately $0.5M on GB200). By scaling up 10x to $4M, the model meaningfully outperforms all publicly available open base models on perplexity evaluations, demonstrating that algorithmic efficiency can enable frontier model development without access to massive compute clusters.