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English(EN) MiniMax M3.1 Flash: How a 428B MoE Model Cuts Agentic AI Costs by 70% Without Sacrificing Performance

MiniMax M3.1 Flash 使用 428B MoE 模型将 AI 代理成本降低 70%

作者 PulseAugur 编辑部 · [1 个来源] ·

MiniMax 发布了其 M3.1 Flash 模型，这是一种拥有 4280 亿参数的混合专家（MoE）架构，旨在显著降低 Agentic AI 应用的成本。该模型通过每个 token 只激活其参数的一小部分来实现这一点，与前代产品相比，成本降低了 70%，速度超过了每秒 200 个 token。关键创新包括用于处理长上下文窗口的 MiniMax Sparse Attention 和用于更快生成的 DSpark speculative decoding，使其能够处理复杂的多步任务。 AI

影响 通过优化推理成本和上下文窗口处理，使复杂、多步任务的 AI 代理更具成本效益和速度。

排序理由 MiniMax M3.1 Flash 模型发布，附带系统卡。[lever_c_demoted from frontier_release: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

MiniMax M3.1 Flash 使用 428B MoE 模型将 AI 代理成本降低 70%

本文如何被排名

Signal score
41 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
MiniMax M3.1 Flash 模型发布，附带系统卡。[lever_c_demoted from frontier_release: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Prabhakar Chaudhary ·

    MiniMax M3.1 Flash: How a 428B MoE Model Cuts Agentic AI Costs by 70% Without Sacrificing Performance

    <h1> MiniMax M3.1 Flash: How a 428B MoE Model Cuts Agentic AI Costs by 70% Without Sacrificing Performance </h1> <p>MiniMax released <a href="https://www.marktechpost.com/2026/10/19/minimax-m3-1-flash-release/" rel="noopener noreferrer">M3.1 Flash</a> on October 19, 2026, and the…

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