Magic claims 50x pretraining efficiency: matches DeepSeek V4 Pro for ~$0.5M
magicailabs · x · 2026-09-09
Magic published a research update claiming its pretraining recipe is >10x more compute-efficient than leading open-weight base models: it matches DeepSeek V4 Pro Base with 50x fewer FLOPs (half of GPT-3's pretraining compute, $0.5M on GB200), and scaling 10x more ($4M) meaningfully beats all public open base models on perplexity. Its scaling laws imply DeepSeek's recipe would cost >$100M to reach the same capability. Evaluated bits-per-byte loss across DeepSeek, Kimi and NVIDIA base models on GB200/GB300 with vLLM and SGLang.
Related event: Magic Claims to Match DeepSeek V4 Pro Pretraining with 1/50 the Compute(11 posts)→
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