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English(EN) On coding tasks, Mistral Large 4 scores 61.7% on DeepSWE v1.1—trailing both open models from China (around 69%) and closed models from OpenAI, Google, and Anthr

Mistral Large 4 在 DeepSWE v1.1 编码基准测试中落后于竞争对手

作者 PulseAugur 编辑部 · [1 个来源] ·

Mistral Large 4 在编码任务的 DeepSWE v1.1 基准测试中取得了 61.7% 的性能。这一分数使其落后于中国开放权重模型（约 69%）以及来自 OpenAI、Google 和 Anthropic 的领先闭源模型（约 74%）。数据显示，即使开放权重模型的发展和可用性不断提高，性能差距依然存在。 AI

影响 Mistral Large 4 在编码基准测试中的表现表明，与领先的闭源模型和部分开放权重模型相比，仍有改进空间。

排序理由 该条目报告了特定 AI 模型的基准测试结果，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

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Mistral Large 4 在 DeepSWE v1.1 编码基准测试中落后于竞争对手

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该条目报告了特定 AI 模型的基准测试结果，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · schuler ·

    在编码任务上，Mistral Large 4在DeepSWE v1.1上得分61.7%——落后于中国的开源模型（约69%）以及来自OpenAI、Google和Anthr的闭源模型

    On coding tasks, Mistral Large 4 scores 61.7% on DeepSWE v1.1—trailing both open models from China (around 69%) and closed models from OpenAI, Google, and Anthropic (around 74%). Performance gaps persist even as open-weight models expand. https://www. implicator.ai/mistral-large-…

    链接 implicator.ai/mistral-large-4-open-weight…

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