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MiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical

MiniMax M3 introduces sparse attention to make long-horizon agents practical, according to a post by elvis@omarsar0. The model aims to improve efficiency for AI agents operating over extended timeframes, contrasting with the recent focus on GLM 5.2 versus Opus comparisons.

read1 min views2 publishedJul 21, 2026
MiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical
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https://t.co/v9huIornsf

elvis@omarsar0ArticleMiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical GLM 5.2 has taken over much of the AI timeline lately, and most of the conversation has centered on how it stacks up against Opus. That is the headline. The workload tells a quieter story: the...2:04 PM · Jul 7, 2026202.1KViews5056065605673073Read 5 replies

source & further reading
twitter.com — original article
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