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English(EN) 📊 GLM-5.3 (Low) scores 36.6% on Humanity's Last Exam while running at 75.7 tokens/sec. The standout is 10.7 intelligence points per dollar — a real cost-efficie

GLM-5.3 (Low) 在 Humanity's Last Exam 上获得 36.6% 的成绩，显示出成本效益

作者 PulseAugur 编辑部 · [1 个来源] ·

GLM-5.3 (Low) 模型在 Humanity's Last Exam 基准测试中取得了 36.6% 的分数。值得注意的是，它在每秒 75.7 个 token 的运行速度下，展现了每美元 10.7 个智能点的成本效益。 AI

影响 展示了大型语言模型的特定成本效益指标，可能影响未来的模型开发和部署策略。

排序理由 在社交媒体平台上发布的模型基准测试结果。[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 →

GLM-5.3 (Low) 在 Humanity's Last Exam 上获得 36.6% 的成绩，显示出成本效益

本文如何被排名

Signal score
12 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
在社交媒体平台上发布的模型基准测试结果。[lever_c_demoted from research: 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, other
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. Mastodon — mastodon.social TIER_1 English(EN) · opensourceaitech ·

    📊 GLM-5.3 (Low) scores 36.6% on Humanity's Last Exam while running at 75.7 tokens/sec. The standout is 10.7 intelligence points per dollar — a real cost-efficie

    📊 GLM-5.3 (Low) scores 36.6% on Humanity's Last Exam while running at 75.7 tokens/sec. The standout is 10.7 intelligence points per dollar — a real cost-efficiency figure, measured independently. That's the math worth checking. https:// olud.ai/leaderboard.html # LLM # Benchmarks…

    链接 olud.ai/leaderboard.html

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