稀宇科技技術報告揭示，M2系列模型無法準確輸出“馬嘉祺”等特定人名，根源在於分詞器導致的“詞元偏移”問題。模型將名字切分爲“馬”和“嘉祺”，使得向量空間被擠壓，造成識別偏差。這暴露了當前大模型訓練中一個普遍但隱蔽的缺陷，影響了特定人名的精確生成。
MiniMax M2系列模型在輸出“馬嘉祺”時出現異常，原因是後訓練階段導致低頻Token退化。排查發現，分詞器將“馬嘉祺”切分爲[馬，嘉祺]，“嘉祺”因預訓練高頻被合併爲獨立Token（編號190467），但在後續訓練中該Token被“擠壓”，引發輸出錯誤。官方已發佈技術報告，揭示了這一機制。
MiniMax開源其自我迭代模型M2.7，華爲昇騰AI同日宣佈完成0Day適配，開發者可在昇騰Atlas系列產品上無縫部署。該模型核心突破在於其智能體能力，通過將早期版本引導爲研究型Agent，使模型具備自我進化與迭代循環功能。
MiniMax 發佈 M2.5 模型，這是其 M2 系列在 108 天內的第三次升級。模型開源權重同步上線魔搭 ModelScope，在編程、搜索、辦公等多場景表現領先，實現了能力、效率與成本的三重突破。該版本提供從零代碼使用到私有化部署的全流程方案，並附有工具調用與推理參數調優的實戰指南，旨在推動低成本 Agent 時代的到來。M2.5 在多項評測中成績突出，例如在 SWE-Bench Verified 中表現亮眼。
Google
$0.49
輸入tokens/百萬
$2.1
輸出tokens/百萬
1k
上下文長度
Openai
$2.8
$11.2
Xai
$1.4
$3.5
2k
$7.7
$30.8
200
-
Anthropic
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$525
$0.7
$7
$35
$17.5
$21
Alibaba
$1
$10
256
$6
$24
$2
$20
$4
$16
Baidu
128
Bytedance
$1.2
$3.6
4
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