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English(EN) MiniMax M2.7 Is Open Source, and It's Rewriting Its Own Code

MiniMax M2.7:开源模型在 SWE-Pro 上媲美 GPT-5.3-Codex,并重写自身代码

MiniMax 发布了 M2.7,一个拥有 2290 亿参数的开源模型,根据 Apache 2.0 许可协议发布。该模型展示了强大的能力,在 SWE-Pro 基准测试中取得了 56.22% 的分数,与 OpenAI 的 GPT-5.3-Codex 持平。值得注意的是,M2.7 参与了自身的开发,在 100 多轮中自主优化了其训练基础设施和代码,使其编程脚手架提高了 30%。 AI

影响 此次发布标志着模型开发新范式的出现,人工智能积极参与自身的训练和迭代,有望加速研发周期。

排序理由 具有重要基准性能和新颖的自我进化能力的 Frontier 模型开源发布。[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 M2.7:开源模型在 SWE-Pro 上媲美 GPT-5.3-Codex,并重写自身代码

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Signal score
48 / 100
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Newsworthiness bucket
Significant
具有重要基准性能和新颖的自我进化能力的 Frontier 模型开源发布。[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
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) · Dishant Sharma ·

    MiniMax M2.7 开源,并正在重写自己的代码

    <p>MiniMax dropped M2.7 on Hugging Face a few hours ago. 229 billion parameters, full weights available, Apache 2.0 license. Within minutes, someone on r/LocalLLaMA was already asking how to run it on a consumer GPU.</p> <p>Here's the number that actually stuck with me: M2.7 scor…