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English(EN) The gap between open-weight GLM 5.3 (44.8) and proprietary Claude Sonnet 5.5 (56) is just 11.2 points—but GLM is 7x cheaper per 1M output tokens. That price-per

开源模型 GLM 5.3 在性能上可与 Claude Sonnet 5.5 相媲美,成本节省 7 倍

开源模型 GLM 5.3 在性能上表现出与闭源模型 Claude Sonnet 5.5 相媲美的竞争力,两者得分仅相差 11.2 分。值得注意的是,GLM 5.3 的成本效益显著更高,每百万输出 token 的成本低七倍。这种强大的性价比使 GLM 5.3 成为一个有吸引力的选择。 AI

影响 凸显了开源模型在性能和成本方面日益增长的可行性,可能影响企业的采用策略。

排序理由 模型性能和成本效益的比较。[lever_c_demoted from research: ic=1 ai=1.0]

在 Mastodon — mastodon.social 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

开源模型 GLM 5.3 在性能上可与 Claude Sonnet 5.5 相媲美,成本节省 7 倍

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Tool
模型性能和成本效益的比较。[lever_c_demoted from research: ic=1 ai=1.0]
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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
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报道来源 [1]

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

    开源 GLM 5.3 (44.8) 与专有 Claude Sonnet 5.5 (56) 的差距仅为 11.2 分——但 GLM 每百万输出 token 的成本便宜 7 倍。每 token 价格

    The gap between open-weight GLM 5.3 (44.8) and proprietary Claude Sonnet 5.5 (56) is just 11.2 points—but GLM is 7x cheaper per 1M output tokens. That price-performance ratio is hard to ignore. https:// olud.ai/leaderboard.html # OpenSource # AI # LLM