AI BriefingMiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical
Key point
MiniMax M3 uses Sparse Attention technology to dramatically reduce cost and computation even with long contexts, enabling the implementation of long-horizon agents.
Details
While GLM 5.2 and similar models have been the hot topic in the AI industry recently, among actual builders it's MiniMax M3 that's drawing attention for its overwhelming cost-performance. Based on Open Router, M3's token usage is over 50% higher than GLM 5.2, showing just how practically useful it is.
The biggest constraint on existing long-horizon agents has been the Attention cost that grows exponentially as context gets longer. M3 solves this problem through MiniMax Sparse Attention (MSA) technology while supporting a long 500K-token context window.
Instead of re-reading all data, MSA selectively reads only the blocks most relevant to the current token via an Index Branch. This provides the following benefits:
- Predictable cost: The main Attention budget stays constant even as context grows longer
- Overwhelming efficiency: Reduces per-token computation (FLOPs) by 28.4x compared to Dense GQA on long sequences
- Fast performance: On H800, Prefill speed improves by 14.2x and Decoding speed by 7.6x
- Maintained performance: Keeps performance similar to full-attention models on major benchmarks like MMLU and GSM8K
This technical advance enables Self-improving agents that can maintain their own state over hours while carrying out complex tasks.
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