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English(EN) MiniMax M3.1 Flash Thinks at Max by Default: First Call, Effort as the Price Knob, and Cache Math vs DeepSeek Flash

MiniMax M3.1 Flash 发布,速度和成本均有提升,但幻觉率较高

MiniMax 推出了其 M3.1 Flash 模型,与前几代产品和 DeepSeek 等竞争对手相比,在速度和成本方面都有显著改进。该模型提供 100 万个 token 的上下文窗口,并支持多模态输入,定价为每 100 万个 token 输入/输出 0.15 美元/0.60 美元。然而,早期报告显示其幻觉率很高,模型在不确定时经常猜测,而不是表示不知道。 AI

影响 此次发布为智能体任务的成本和推理速度设定了新的基准,可能会迫使竞争对手匹配其性能和定价。

排序理由 来自前沿实验室(MiniMax)的新模型发布。[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 发布,速度和成本均有提升,但幻觉率较高

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
来自前沿实验室(MiniMax)的新模型发布。[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, product
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
6 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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) · Yong Yu ·

    MiniMax M3.1 默认闪电思考:首呼、以努力为价格旋钮,以及缓存数学 vs DeepSeek Flash

    <blockquote> <p><strong>Attributed compile + two small offline scripts</strong><br /><br /> <strong>Primary sources (MiniMax, 2026-10-19):</strong> <a href="https://www.minimax.io/blog/minimax-m31f" rel="noopener noreferrer">MiniMax M3.1 Flash: Redefining Agent Performance and Co…