本ページの本文は英語で提供されています。タイトルと導入は日本語化されています。
これは何か
MiniMax-M1 is an open-weight reasoning model MiniMax released in June 2025. MiniMax was founded in Shanghai in 2021 by Yan Junjie and others. M1 uses a hybrid attention design that combines standard full-attention layers with linear-attention layers to cut the compute cost of long-sequence reasoning, and its stated context window reaches the million-token range. With 456B total parameters activating about 46B per token, it is a large-scale MoE reasoning model whose weights are released under an open licence. The company also runs generation product lines such as Hailuo.
なぜ覚えておく価値があるか
It mixes linear and full attention and pushes context to a million tokens to cut the cost of long-sequence reasoning; among the larger open-weight reasoning models, it is a concrete landing of the open-weight camp on the reasoning track.
主な仕様
- Parameters
- 456B (MoE, ~46B active)
- Context window
- 1M tokens
- Architecture
- Hybrid linear and full attention
- Open weights
- Yes
対応する能力
関連する概念
同種の製品
DeepSeek-R1
2025強化学習で推論の連鎖を訓練し、MIT ライセンスで重みを公開した推論モデル
Qwen
2023多様な規模とマルチモーダル版を備えたオープンウェイトのモデル群
o3
2025答える前に長い推論を重ね、推論時の計算で正答率を上げる
GLM
2023自己回帰空白穴埋め事前学習から始まった中国語の汎用モデル
Doubao
2023バイトダンスの汎用対話モデルとアプリ
Step
2023マルチモーダルとオンデバイスを狙う汎用モデル群