MiniMax‑M1基于 1,000,000 个令牌上下文的开源大规模推理模型，具有混合的 MIXTURE-OF-EXPERTS 架构。

4.4 (5)

审阅者 Daniel Nikulshyn·更新 2026年6月

Open Source Large Language Model Reasoning Long Context Mixture-of-Experts Research AI Model

概览

MiniMax-M1 是一个开源加权、大规模的混合注意力推理模型。它采用混合 Mixture-of-Experts（MoE）架构与闪电级注意力机制相结合，支持高效扩展推理时计算量。该模型原生支持 100 万 token 的上下文长度，并使用大规模强化学习（RL）在多样化任务上进行训练。它在复杂软件工程、工具使用以及长上下文任务上表现优于其他强大的开源加权模型。在标准基准测试中，MiniMax-M1 在数学、编码、软件工程、代理工具使用以及长文本理解等领域的任务上，表现优于其他模型。该模型特别适合需要处理长输入并进行深入思考的复杂任务。 MiniMax-M1 作为下一代语言模型代理的坚实基础，支持其推理并应对现实世界的挑战。领先商业模型与开源加权模型在不同类别任务中的基准性能比较凸显了该模型的表现。技术报告提供了关于模型架构、训练协议和评估结果的更多信息。

主要功能

Hybrid Mixture-of-Experts （MoE）架构
闪电式注意力机制
强化学习（RL）尺寸框架
1,000,000 个令牌的上下文长度
测试时间计算的有效扩展

价格

模型: Free
分类: AI Agent Development Frameworks
评分: 4.4 / 5 (5)

使用场景

长文档分析

利用 1,000,000 个令牌的上下文窗口来分析单文件或分段的文本、合同或代码库。"

复杂推理任务

利用模型的大规模推理能力来解决数学、逻辑和研究场景中的多步问题。"

自主部署的 AI

在私有基础设施上部署开源模型，以实现完整的数据控制、自定义以及成本效益推理。"

研究和微调

利用开源权重来进行 AI 研究，或微调模型来实现领域专门化的应用。"

优点 & 缺点

优点

启用测试时间计算的有效扩展
在复杂的软件工程、工具使用和长上下文任务中出色地表现出其他强开源模型的超越
原生支持 1,000,000 个令牌的上下文长度
使用大规模的强化学习 (RL) 对多样化问题进行训练

缺点

有限的有关模型的训练数据和参数的信息
可能不会对其训练域外的任务产生广泛的效果
训练和测试需要大量的计算资源

评测

4.4

5 个评分的平均值。

登录以留下评测。

Liam O’Connor

Oct 8, 2025

Does the job

Pretty happy overall. The API just works and it saves real time. but no dealbreakers — I'd recommend it to a friend without hesitating.

Beatriz Costa

Oct 7, 2025

Solid for our team

We rolled this out across the team last quarter and the value for money is strong. The automation fits neatly into how we already work, and the dashboard removed a step we used to do by hand. The docs could be deeper, which is the main caveat, but it has held up under daily use.

Victor Nguyen

Jul 21, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: the dashboard and support is responsive. On balance the feature set — especially the API — justifies the 5 stars for our use case.

Mei-Ling Wong

Jul 10, 2025

Does the job

Pretty happy overall. The API just works and it saves real time. Pricing gets steep at scale can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Diego Fernández

Jun 5, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is the dashboard — handled better than most — and it is genuinely easy to set up. A few rough edges remain is my one real gripe. Worth the time if this is your use case.

问答

How large of a context window does MiniMax-M1 support?

MiniMax-M1 supports a context window of up to 1 million tokens, making it suitable for tasks that involve very long documents, extended conversations, or large codebases that require reasoning across substantial amounts of input.

Is MiniMax-M1 free to use since it's open-source?

MiniMax-M1 is released as an open-source model, which generally means the weights and code are publicly available. However, you should review its specific license terms to confirm permitted commercial or derivative use.

What is MiniMax-M1 and what makes its architecture notable?

MiniMax-M1 is an open-source large-scale reasoning model featuring a hybrid Mixture-of-Experts (MoE) architecture. Its design is aimed at efficient large-scale reasoning while remaining openly available for use and modification.